Patent Publication Number: US-11650748-B1

Title: Method of delayed execution of eBPF function in computational storage

Description:
BACKGROUND 
     A majority of computer readable instructions that are executed by a computing device are operations that move data. Therefore, a majority of power consumption is spent not on performing relevant computations, but rather, on moving data between a processing core and memory of the computing device. Such inefficiencies reduce performance of metadata and user data operations and can shorten the lifetime of computing device memory on which a relatively high amount of read and write instructions are being performed. 
     It is with respect to these and other general considerations that aspects of the present disclosure have been described. Also, although relatively specific problems have been discussed, it should be understood that the embodiments disclosed herein should not be limited to solving the specific problems identified in the background. 
     SUMMARY 
     Aspects of the present disclosure relate to methods, system, and media for offloading data processing into computational storage. 
     In one aspect of the present disclosure, a method for offloading data processing into computational storage is provided. A request to offload data computation into computational storage is received. One or more transactions to encapsulate the request are prepared. One or more write requests are generated based on the one or more transactions, and the one or more transactions are stored into one or more journals. A set of transactions is extracted from the one or more journals. A subset of the set of transactions is received at an eBPF subsystem, where the subset corresponds to one or more computation requests. Information from a file is extracted, where the information corresponds to one or more logical block addresses (LBAs). The one or more computation requests are performed on the one or more LBAs using the subset of the set of transactions, and an indication corresponding to the performed computation requests is generated. 
     In another aspect, a system for offloading data processing into computational storage is provided. The system comprises a first device comprising a computational component; a second device comprising a computational component; and memory storing instructions. When the instructions are executed by the computational component of at least one of the first device and the second device, the instructions cause the system to perform a set of operations comprising: receiving, via the computational component of the first device, a request to offload data computation into the second device; preparing, via the computational component of the first device, one or more transactions to encapsulate the request; generating, via the computational component of the first device, one or more write requests based on the one or more transactions; storing, via the computational component of the first device, the one or more transactions into one or more journals of the first device; extracting, via the computational component of the second device, a set of transactions from the one or more journals; receiving, via an eBPF subsystem of the second device, a subset of the set of transactions, the subset corresponding to one or more computation requests; extracting, via the computational component of the second device, information from a file, the information corresponding to one or more logical block addresses (LBAs); performing, via the computational component of the second device, the one or more computation requests on the one or more LBAs using the subset of the set of transactions; and generating, via the computational component of the second device, an indication corresponding to the performed computation requests. 
     This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Non-limiting and non-exhaustive examples are described with reference to the following Figures. 
         FIG.  1    illustrates an overview of a conventional system without computational storage. 
         FIG.  2 A  illustrates an example system for offloading data processing into computational storage according to some aspects described herein. 
         FIG.  2 B  illustrates an example system for offloading data processing into computational storage according to some aspects described herein. 
         FIG.  2 C  illustrates an example system for offloading data processing into computational storage according to some aspects described herein. 
         FIG.  3    illustrates an example system for offloading data processing into computational storage according to some aspects described herein. 
         FIG.  4    illustrates an example system for offloading data processing into computational storage according to some aspects described herein. 
         FIG.  5    illustrates an example system for offloading data processing into computational storage according to some aspects described herein. 
         FIG.  6    illustrates a schematic diagram of a trigger execution approach in a system for offloading data processing into computational storage, according to some aspects described herein. 
         FIG.  7    illustrates a schematic diagram of registering an eBPF function according to some aspects described herein. 
         FIG.  8    illustrates a schematic diagram of registering an event of eBPF function execution according to some aspects described herein. 
         FIG.  9    illustrates a schematic diagram of journaling according to some aspects described herein. 
         FIG.  10    illustrates a process for offloading data processing into computational storage according to some aspects described herein. 
         FIG.  11    illustrates a schematic diagram of offloading data processing into computational storage according to some aspects described herein. 
         FIG.  12    illustrates a schematic diagram of offloading data processing into computational storage according to some aspects described herein. 
         FIG.  13    illustrates a schematic diagram of offloading data processing into computational storage according to some aspects described herein. 
         FIG.  14    is a block diagram illustrating physical components of a computing device with which aspects of the disclosure may be practiced. 
         FIG.  15 A  illustrates a mobile computing device with which embodiments of the disclosure may be practiced. 
         FIG.  15 B  is a block diagram illustrate the architecture of one aspect of a mobile computing device. 
         FIG.  16    illustrates an exemplary tablet computing device that may execute one or more aspects disclosed herein. 
     
    
    
     DETAILED DESCRIPTION 
     In the following Detailed Description, references are made to the accompanying drawings that form a part hereof, and in which are shown by way of illustrations specific embodiments or examples. These aspects may be combined, other aspects may be utilized, and structural changes may be made without departing from the present disclosure. Embodiments may be practiced as methods, systems or devices. Accordingly, embodiments may take the form of a hardware implementation, an entirely software implementation, or an implementation combining software and hardware aspects. The following detailed description is therefore not to be taken in a limiting sense, and the scope of the present disclosure is defined by the appended claims and their equivalents. 
     Various examples illustrating aspects of the present disclosure are described herein. Across examples, components may be described with similar names (e.g., journal, core, zone, NAND die or die, volume, file system, etc.). It should be recognized that components with similar names, and described in different examples, may be capable of performing similar functions or interacting with other components in similar manners. Alternatively, in some examples, components with similar names, and described in different examples, may be capable of performing different functions or interacting with different components than the earlier/later described components with the similar names. 
     As mentioned above, a majority of computer readable instructions that are executed by a computing device are operations that move data. Therefore, a majority of power consumption of a computing device is spent not on performing relevant computations, but rather on moving data between a processing core and memory of the computing device. Such inefficiencies reduce performance of metadata and user data operations and can shorten the lifetime of computing device memory on which a relatively high amount of read and write instructions are being performed. 
     The rise of big data sets in industry and the need for larger memory volumes in computing technology applications have created inefficiencies in data processing that are time-consuming and power consuming. Generally speaking, 80% of machine instructions are operations that move data from one location to another location. Therefore, the majority of power consumption in a data processing system is spent not on relevant computations, but rather on moving data and/or instructions between a processing core and memory. 
     Central processing unit (CPU) caches may improve data processing performance, but as a side effect, the caches need to employ complicated cache coherence protocols to achieve a consistent view of data in memory, using cores of the central processing unit. Further, CPU caches may be built on static random-access memory (SRAM) that is relatively fast, but also consumes a relatively large quantity of power. Dynamic random-access memory (DRAM) can also consume a relatively large quantity of power (e.g., since cells of DRAM are refreshed every 64 to 32 milliseconds to keep data). So, increasing a capacity of DRAM or CPU cache size can result in an increase in power consumption. On the other hand, persistent memory does not need to refresh memory cells and is therefore much more power-efficient. Some computing systems require moving data from persistent storage into DRAM with the goal to access and process data by CPU cores. Persistent memory technologies continue to become faster for computations; however, modern computing systems negate the advantages being made in persistent memory technologies because of known drawbacks. 
     File storage systems may contain information that is stored in persistent memory of the associated storage devices. For a host device to perform actions that are based on the information stored in the persistent memory of the associated storage device, the information has to first be retrieved from the persistent memory (e.g., a read operation needs to be performed) of the storage device into DRAM on the host side, then the CPU core on the host side can execute some function (execute some computation) based on the retrieved information. The result of the computation executed by the CPU core on the host side then must be stored from the DRAM on the host side into the persistent memory of the storage device. Using such conventional implementations not only requires extensive data moving and/or data exchange operations between the host device and the storage device, but also requires extensive moving operations between the DRAM and L caches of the CPU cores on the host side. 
     Aspects of the present disclosure address the above-mentioned deficiencies, in addition to further benefits which may be recognized by those of ordinary skill in the art. For example, using systems and mechanisms described herein, data processing can be offloaded from a host device to a storage device (e.g., a computational storage device). Accordingly, data and metadata can be processed in persistent memory space, without depleting computational resources of the host device. Generally, methods and systems disclosed herein provide powerful techniques to offload data processing onto a computational storage device that interacts with a host device. 
     More specifically, field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), or RISC-V core(s) may retrieve data, execute a computation or function, and return results of the computation or function, all inside of the same device (e.g., a computational storage device). Some advantages may be that: (1) data moving operation on a host side are excluded, (2) data processing inside of computational storage can be executed by multiple FPGA cores in parallel, and (3) results of operation, such as computational operations, can be stored into persistent memory by a computational storage device itself. All of these points improve performance because a host device does not spend resources on data moving operations. 
       FIG.  1    illustrates an overview of a conventional system  100  without computational storage. The system  100  include a host device  102  and a storage device  104  (e.g., a solid-state drive (SSD) or a hard disk drive (HDD)). The host device  102  may be coupled to, or otherwise in communication with, the storage device  104 . The host device  102  includes a processor  106  (e.g., a central processing unit (CPU)). The processor  106  may include a cache  108 . The cache  108  may store a local copy of data that is used by the processor  106  for executing functions for host device  102 . The host device  102  further includes memory  110  (e.g., dynamic random-access memory (DRAM)). The processor  106  may be coupled to and/or in communication with the memory  110  to execute read and/or write instructions for data operations. The host device  102  may further be in communication with and/or include a graphical processing unit (GPU)  112 . The graphical processing unit  112  may also be coupled to and/or in communication with the memory  110  to execute read and/or write instructions for data operations. The storage device  104  further includes a controller  114 . 
     A controller, as described with respect to conventional systems discussed herein, refers to a system that may include a component, such as an application-specific integrated circuit (ASIC), that manages read and/or write operations using input-output (I/O) systems. The combination of a controller and persistent memory, as discussed with respect to  FIG.  1   , differs from computational storage devices discussed below, at least because a computational storage device utilizes a computational component to perform computations, or other processing, on data that is already stored in the computational storage device. That is, the computational storage device can receive instructions to process, or perform computations, on the data in the computational storage device that is already stored at the computational storage device. As an example, one or more instructions may be sent to the computational storage device to execute some computation inside of computational storage device on data that is already stored at the computational storage device. Such data may be stored or provided to the computational storage device by means of standard I/O operations from a host or controller; however, the computational storage device is configured to receive one or more instructions, from the host for example, and perform a computation on the data, where the computation goes beyond accessing, moving, or storing the data. For example, the computational storage device may perform computations, including but not limited to Boolean computations, arithmetic computations, logical computations, interference computations, etc. Alternatively, or in addition, if data is stored at the host side then, such computations may be performed at the host side. However, if the data is already stored in the computational storage device, then from a computation and efficiency perspective, it makes sense to offload the computation by performing the computations via the computational storage device. In examples, a computational storage device may include a field-programmable gate array (FPGA), ASIC, or RISC-V to perform such computations. 
     Aspects of the system  100  may exemplify common issues that are faced using conventional data storage methods. For example, caches (e.g., cache  108 ) may experience cache coherence problems where data that is stored across multiple local caches are not properly synchronized as the processor  106  updates local copies of data (e.g., after performing write and/or update operations). Further, memory (e.g., memory  110 ) may face a memory wall problem, such as occurs when the rate of improvement of processor performance far exceeds the rate of improvement in DRAM memory speed. Memory wall problems can be a performance bottleneck in systems operations. The system  100  may experience a throughput bottleneck as data is transferred between the host device  102  to the storage device  104 . A throughput bottleneck can limit productivity and efficiency of the system  100 . 
     System  100  may further experience data moving problems when transmitting data between the host device  102  (e.g., from memory  110 ) and the GPU  112 . For example, transmitting data between the host device and GPU  112  may create a power consumption problem where the GPU demands a relatively large or undesirable amount of power from system  100  to receive, and/or perform operations using, data from the host device  102 . Excessive data movement can reduce the lifetime of hardware components that store data (e.g., an SSD or HDD), in addition to reducing the efficiency of a system in which the data movement is occurring (e.g., system  100 ). Therefore, it may be beneficial to implement systems and methods in which data movement is reduced to perform desired actions or computations. 
     System  100  may further experience excess controller overhead at controller  114  when the controller  114  is used to manage a relatively large amount of data operations. Generally, the storage device  104  may experience big data problems, in which relatively large amounts of data and/or metadata are stored on the storage device  104 . Conventional storage devices lack computational capabilities. Therefore, computations cannot be offloaded to such conventional storage devices. Accordingly, mechanisms disclosed herein that allow computations to be offloaded into a storage device are beneficial. 
       FIGS.  2 A- 2 C  illustrate example systems for offloading data processing into computational storage according to some aspects described herein. In some examples, the storage device can be implemented as a storage device with embedded computation (e.g., a smart solid-state drive (SmartSSD), a smart network interface card (smartNIC), an intelligence processing unit (IPU), a specialized hardware accelerator, etc.). The responsibility of the host device may be to deliver an executable function to the storage side device (e.g., via an extended Berkeley packet filter (eBPF)) and initiate data processing to occur within the storage device (e.g., computational storage). 
       FIG.  2 A  illustrates an example system  200  for offloading data processing into computational storage. The system  200  includes a host device  202  and a hardware architecture or hardware accelerator  204 . In the example system  200 , the hardware accelerator  204  is a programmable accelerator, such as a smartNIC, or an IPU. The hardware architecture  204  can include a computational storage device, such as computational storage device  234 , and/or  264  discussed further below. For example, the hardware architecture  204  can include a storage component, such as one or more solid-state drives (SSDs). Further, the hardware architecture  204  can include a computational component, such as an FPGA. The storage component and computational component may form the computational storage device on the hardware architecture  204 . 
     The hardware architecture  204  may be configured to receive network packets. The hardware architecture  204  can include an operating system (OS) kernel or kernel-space (e.g., Linux kernel), and one or more eBPF subsystem(s)  206  that are configured to execute instructions within the kernel. Alternatively, the one or more eBPF subsystem(s)  206  can be implemented on hardware only (e.g., hardware of the smartNIC or IPU). Further, the host device  202  can include a kernel or kernel-space, and one or more eBPF subsystem(s)  206  that are configured to execute instructions within the kernel. Additionally, or alternatively, the host device  202  can include a user-space, and the one or more eBPF subsystem(s)  206  can be configured to execute instructions within the user-space. One of ordinary skill in the art will recognize types of eBPF subsystem(s)  206  that can be configured to be executed by the host device  202  and/or the hardware architecture  204 . 
     The hardware architecture  204  may be configured to analyze and/or parse contents of network packets to determine results corresponding to the received network packets. For example, the host  202  may send network packets to the hardware architecture  204 , and the hardware architecture  204  (e.g., a SmartNIC) may send the network packets to another host. Alternatively, the hardware architecture  204  (e.g., a SmartNIC) may receive the network packets from another host, analyze the networks packets, and deliver a result of the analysis to the host  202 . The hardware architecture  204  may further be configured to process data corresponding to the determined results, and store results into non-volatile memory or persistent memory (e.g., flash memory). In this regard, data processing may be offloaded from the host device  202  to the hardware architecture  204 . 
       FIG.  2 B  illustrates an example system  230  for offloading data processing into computational storage. The system  230  includes a host device  232  and a computational storage device  234  (e.g., a combination of a hardware accelerator and one or more storage devices). In the example system  230 , the computational storage device  234  is an integrated circuit, such as an FPGA, ASIC, or RISC-V that includes one or more solid state drives (SSD). For example, the FPGA may be coupled to the one or more solid state drives (SSD). The computational storage device  234  may be configured to receive network packets. The computational storage device  234  can include a kernel or kernel-space (e.g., a Linux OS kernel-space), and one or more eBPF subsystem(s)  236  that are configured to execute instructions within the kernel. Alternatively, the one or more eBPF subsystem(s)  236  can be implemented on hardware only (e.g., hardware of the FPGA). Further, the host device  232  can include a kernel or kernel-space, and one or more eBPF subsystem(s)  236  that are configured to execute instructions within the kernel. Additionally, or alternatively, the host device  232  can include a user-space, and the one or more eBPF subsystem(s)  236  can be configured to execute instructions within the user-space. One of ordinary skill in the art will recognize types of eBPF subsystem(s)  236  that can be configured to be executed by the host device  232  or the computational storage device  234 . 
     The computational storage device  234  may be configured to analyze instructions (e.g., received from the host device  232 ) to determine functions or computations corresponding to the received instructions. The computational storage device  234  may further be configured to process data corresponding to the determined results, and store results into non-volatile memory or persistent memory (e.g., flash memory). In this regard, data processing may be offset from the host device  232  to the computational storage device  234 . 
       FIG.  2 C  illustrates an example system  260  for offloading data processing into computational storage. The system  260  includes a host device  262  and a storage device  264 . In the example system  260 , the storage device  264  is a computational storage device. The storage device  264  may be configured to receive instructions. The storage device  264  can include an operating system kernel or kernel-space, and one or more eBPF subsystem(s)  266  that are configured to run within the kernel. Alternatively, the one or more eBPF subsystem(s)  266  can be implemented on hardware only (e.g., hardware of the computational storage device  264 ). Further, the host device  262  can include a kernel or kernel-space, and one or more eBPF subsystem(s)  266  that are configured to execute instructions within the kernel. Additionally, or alternatively, the host device  262  can include a user-space, and the one or more eBPF subsystem(s)  266  can be configured to execute instructions within the user-space. One of ordinary skill in the art will recognize types of eBPF subsystem(s)  266  that can be configured to be executed by the host device  262  or the storage device  264 . 
     The storage device  264  may be configured to analyze instructions (e.g., received from the host device  262 ) to determine functions or computations corresponding to the received instructions. The storage device  264  may further be configured to process data corresponding to the determined results, and store results into non-volatile memory or persistent memory (e.g., flash memory). In this regard, data processing may be offloaded from the host device  262  to the storage device  264 . 
       FIG.  3    illustrates an example system  300  for offloading data processing into computational storage according to some aspects described herein. The system  300  includes a host device  302  and a computational storage device  304  (sometimes referred to herein simply as storage device for brevity). The host device  302  may include dynamic random-access memory (DRAM)  305 . The host device  302  may further include an operating system user-space  306  and an operating system kernel-space  308 . The user-space  306  correspond to segments of memory that are designated to store computer-readable instructions corresponding to user-space related functions. For example, the user-space  306  may include an application  310 . The application  310  may be a word processing application, a graphics application, a computational application, a modelling application, or any other type of application recognized by those of ordinary skill in the art. 
     Similar to the OS user-space  306 , the OS kernel-space  308  corresponds to segments of memory that are designated to store computer-readable instructions corresponding to kernel-space related functions that may be recognized by those of ordinary skill in the art. The kernel-space  308  may include a file system driver  312  and a block layer  314 . The file system driver  312  may receive data from the application  310 , and the application  310  may transmit data to the file system driver  312  (e.g., via a processor), or vice-versa. The block layer  314  may receive data from file system driver  312  (e.g., via a processor), or vice-versa. The block layer  314  may then transfer or transmit data to the storage device  304 , or vice-versa. 
     The storage device  304  includes a computational component, such as but not limited to an FPGA  316 , and persistent memory  318 . The host device  302  may store data (e.g., user data and/or metadata) in the storage device  304  (e.g., in the persistent memory  318 ), for example, via an ASIC controller. The FPGA  316  may interact with the persistent memory  318  (e.g., via read and/or write instructions) to perform functions or computations (e.g., Boolean computations, arithmetic computations, logical computations, interference computations, etc.), such as functions or computations designated by the kernel-space  308 . The persistent memory  318  may include a file system (FS) volume or volume  320 . The file system volume  320  may store user data and/or metadata related to the file system  312  and/or the application  310 . 
     Generally, file systems provide a way to manage and access data for applications. A file system is a mediator between applications and storage devices. File systems may contain metadata, that is usually unseen by an application, on a file system volume. Information that an application is trying to read or write can be user data. In some instances, user data can contain metadata from an application, that a user can see and modify. 
     The file system driver  312  may contain knowledge about metadata and user data that are stored in the storage device  304  (e.g., in the persistent memory  318  of the storage device  304 ). In this respect, the file system driver  312  can transfer a series of functions to be executed (e.g., compiled in a journal or other data structure) to the storage device  304  on which the functions are executed. Accordingly, the file system driver  312  can offload processing of metadata and user data onto the storage device  304  because the storage device  304  is a computational storage device (e.g., a storage device with hardware-assisted processing capabilities). 
     The file system driver  312  can perform a number of functions. The file system driver  312  can identify addresses in persistent memory  318  (e.g., logical block addresses or LBAs) that need to be processed, for example that need to be processed for the application  310 . The file system driver  312  can identify the type of data that is stored in the addresses in persistent memory  318  (e.g., metadata, user data, etc.). Having knowledge regarding the type of data that is stored in persistent memory  318  that is desired to be accessed can be useful in increasing performance of computational functions performed by a system, such as system  300 . The application  310  can define what functions or algorithms need to be applied on a file. For example, the application  310  can define the function or algorithm on the basis of the particular data type in a file. The file system driver  312  can identify LBAs that contain data corresponding to the file on which the functions or algorithms (as defined by the application  310 ) need to be applied. The application  310  can request an execution of the functions or algorithms for the file, and the file system driver  312  can transmit the functions, with a definition of relevant LBA ranges or sets of LBAs on the computational storage device  304 . The host device  302  can retrieve the result of data processing from the storage device  304 , based on the identifications made, or knowledge stored, by the file system driver  312 . 
       FIG.  4    illustrates an example system  400  for offloading data processing into computational storage according to some aspects described herein. The system  400  may be similar to the system  300 . For example, the system  400  includes a host device  402  and a computational storage device  404 . The host device  402  includes an operating system kernel-space or operating system user-space with one or more eBPF subsystem(s)  406  that are configured to run therein. The host device  402  is coupled to, or otherwise in communication with the storage device  404 . 
     The storage device  404  is a computational storage device. The computational storage device  404  can be the combination of various types of memory. For example, the computational storage device  404  can include one or more from the group of NAND flash, non-volatile memory (NVM), storage class memory (SCM), and dynamic random-access memory (DRAM). The computational storage device  404  can include an operating system kernel with one or more eBPF subsystem(s)  406  that are configured to run therein. Alternatively, the computational storage device  404  can implement the eBPF subsystem(s)  406  on a hardware level. The computational storage device  404  can include one or more FPGA cores  408  (sometimes referred to herein simply as cores, for brevity) that are configured to execute instructions stored in memory. The computational storage device  404  can further include persistent memory (e.g., persistent memory  318  as shown in the example system  3  of  FIG.  3   ) that stores data and/or metadata  410  therein. 
     Generally, persistent memory is the main memory space in which a file system&#39;s metadata and user data is stored persistently. The computational storage device  404  can include dynamic random-access memory (DRAM) as temporary memory or non-volatile memory (NVM) to process I/O requests from the host device  402 . In this respect, the computational storage device  404  can process metadata and user data to offload computational demands from the host device  402 . The one or more cores  408  may be one or more cores of an FPGA, ASIC, or RISC-V, for example. 
     The one or more cores  408  can be configured or otherwise programmed to execute data processing within the computational storage device  404 . For example, the one or more cores  408  may perform computations (e.g., Boolean functions, arithmetic computations, logical computations, interference computations, etc.), in addition or alternatively to read and/or write requests, on data/metadata  410 . The one or more cores  408  can be programmed before data processing requests are received from the host device  402  (e.g., by way of the one or more sequences of instructions, such as eBPF functions). Alternatively, the one or more cores  408  can execute logic instructions (e.g., the eBPF functions) as part of the data processing requests transmitted from the host device  402 . Generally, the host device  402  sends a data processing request to the storage device  404  that can include one or more functions to be performed. The storage device  404  performs the functions, such as by executing instructions, via the one or more cores  408 , that interact with the data/metadata  410  (e.g., by performing computations, such as Boolean functions, arithmetic functions, logic functions, interference functions, etc.). Subsequently, a result of the performed functions is received by the host device  402 , such as after being transmitted from the storage device  404  to the host device  402 . 
       FIG.  5    illustrates an example system  500  for offloading data processing into computational storage according to some aspects described herein. Specifically,  FIG.  5    illustrates a computational storage device  504  that may be similar to the computational storage devices  304  and  404  discussed above and illustrated in  FIGS.  3  and  4   . The computational storage device  504  includes a field programmable gate array (FPGA)  506 , dynamic random-access memory (DRAM)  508 , and persistent memory  510 . Collectively, the FPGA  506 , DRAM  508 , and persistent memory  510  may form a hardware architecture. The FPGA  506  may include one or more cores  512 . The one or more cores  512  may be similar to the core  408  discussed above and illustrated in  FIG.  4   . 
     The persistent memory  510  may include one or more NAND die  514 . Additionally, or alternatively, the persistent memory  510  may include non-volatile memory (NVM)  516 . Additionally, or alternatively, the persistent memory  510  may include storage class memory (SCM)  518 . Additionally, or alternatively, the persistent memory  510  may include a management unit with a flash translation layer (FTL)  520 . One of ordinary skill in the art will recognize that the storage device  504  may include other memory architectures that perform a similar function to the example persistent memory architectures disclosed herein. 
     Data and metadata similar to data and metadata  410  discussed above with respect to  FIG.  4    may be stored within the persistent memory  510 . In this regard, the one or more cores  512  may receive data or metadata from the persistent memory  510  to perform computations or functions (e.g., Boolean computations, arithmetic computations, logical computations, interference computations, etc.). Additionally, or alternatively, the one or more cores  512  may input data or metadata into the persistent memory  510 , either as the result of a computational process, or as an intermediary value in a computational process. 
       FIG.  6    illustrates a schematic diagram of a trigger execution approach in a system for offloading data processing into computational storage, according to some aspects described herein. The example system  600  may be similar to the example system  300  shown in  FIG.  3    and discussed in detail above, and may include, for example, a host device  602  and a computational storage device  604 . In an embodiment, offloading data processing from the host device  602  to the computational storage device  604  can be implemented via a trigger approach. For example, a trigger  630  may be thought of as a descriptor that defines the activity for a particular event  624 . If that event  624  has occurred, then an action  622  will execute under a related piece of data  626  or code  628 . In an embodiment, an application  610  on the host device  602  may request the creation of a trigger in the storage device  604 , and receive as a result of the request a success or failure of trigger creation. The trigger  630  created in the storage device  604  may be executed as a reaction or response to some event (e.g., event  624 ). As an example, the application  610  may request the creation of a trigger (e.g., trigger  630 ) for a read operation in the storage device  604 . Such a trigger will be executed for every (or some portion of) read I/O request received at the storage device  604 . The application  610  would receive the result of the trigger execution as a result of the read I/O request. 
       FIG.  7    illustrates a schematic diagram of registering an eBPF function according to some aspects described herein. The example system  700  may be similar to the example system  300  shown in  FIG.  3    and discussed in detail above, and may include, for example, an application  710  (e.g., on a host device, such as host device  302 ) and a file system  712 . In an embodiment, offloading data processing execution into computational storage may be implemented by registering an eBPF function  766  before execution. Alternatively, in an embodiment, offloading data processing execution into computational storage may be implemented by sending an eBPF function  766  to the computational storage at the timepoint of execution. For example, one way to deliver a function or executable code into computational storage is through the use of an extended attribute of a file  740  and/or folder in the file system  712 . An extended attribute is a binary blob  738  that is associated with a file and/or folder and can be accessed by name. 
     In an embodiment, eBPF function source code  706  may be compiled by means of a special eBPF tool  732 . Alternatively, or additionally, eBPF function source code  706  may be compiled via an application  710  by means of a libeBPF library  736 . It should be noted that compiling eBPF function source code  706  may include converting the initial source code into bytecode that can be executed by the target platform. In an example, the extended attribute of a file  740  and/or folder in the file system  712  may be created by means of setxattr tool  734  or setxattr system call. In an embodiment, the eBPF function  766  may be stored in a dedicated xattr namespace in the file system  712 . 
       FIG.  8    illustrates a schematic diagram of registering an event of eBPF function execution according to some aspects described herein. The example system  800  may be similar to the example system  300  shown in  FIG.  3    and discussed in detail above, and may include, for example, an application  810  (e.g., on a host device, such as host device  302 ) and a file system  812 . In an embodiment, the execution of a registered eBPF function  866  may be immediate (e.g., by request). In another embodiment, the execution of a registered eBPF function  866  may be delayed (e.g., by event, such as event  624  shown in the system  600  of  FIG.  6   ). In an example where execution of the registered eBPF function  866  is delayed, the corresponding trigger event should be recognizable by the target platform. The event  842  can be registered as an extended attribute of a file  840  and/or folder in the file system  812 , in an embodiment. For example, the name of an extended attribute of the file  840  can represent the event  842 . A binary blob  838  of such an extended attribute that is associated with the file  840  should contain the name of the registered eBPF function  866 , or contain the registered eBPF function  866  itself. In an example, the extended attribute of the file  840  may be created by means of setxattr tool  834  or setxattr system call. In an embodiment, the registered event  842  may be stored in a dedicated xattr namespace in the file system  812 . 
       FIG.  9    illustrates a schematic diagram  900  of journaling according to some aspects described herein. The schematic diagram  900  displays a host device  902  with a file system  912  (e.g., a file system driver in a Linux kernel of a host device) and application  910 , and a computational storage device  903 . The computational storage device  903  can include a journal  904 , a field programmable gate array (FPGA)  906 , and a file system volume or volume  908 . Together, the journal  904  and file system volume  908  may constitute a replay journal  930 . The file system  912 , which includes metadata  914  and user data  916 , can prepare journal transactions  920  that are added into the queue of a journaling subsystem, such as a journaling subsystem for the journal  904 . A journaling thread can write the prepared transactions  920  of the journaling subsystem into special partitions or portions of the volume  908 . The prepared transactions  920  may be replayed from the volume  908  after specific events (e.g., failed transactions, system start-up, etc.), or after specific periods of time. The journal subsystem for the journal  904  can read transactions from the journal  904  and write transaction contents into requested physical sectors of memory, such as of the volume  908 . 
     Generally, journaling is a file system technique that may be familiar to those of ordinary skill in the art. The goal of journaling techniques in data computation contexts may be to queue eBPF functions that can be executed by computational storage devices, such as any of the computational storage devices disclosed herein. In some examples, journals (such as journal  904 ) can be implemented as circular buffers that are used to keep a record of data transactions (e.g., data modifications to be performed by a core). Every data transaction may contain a record of a modification of one or more LBAs (e.g., LBAs on a file system volume, such as file system volume  520  or  908 ). 
     Data transactions such as creating metadata  914 , user data  916 , or other types of data may be stored in the journal  904 . The file system driver  912  may flush or store the prepared journal transactions  920  into the file system volume  908 . If a transaction fails (e.g., the transaction is broken or unable to be completed), then the transaction may be discarded from the journal  904 . For example, the journal  904  may be replayed within the computational storage device  903 , without replaying the discarded transaction. Replaying the journal  904  (e.g., via commands executed by the FPGA  906 ) can include creating the actual state of the file system volume  908 , based on events that occur within the computational storage device  903 . 
     Generally, journaling techniques are a flash-friendly approach to executing data operations. For example, when a journal (e.g., journal  904 ) is a circular buffer, there is no update of information in the journal. As a result, journaling techniques incorporated with aspects disclosed herein prolong a lifetime of persistent memory. The journal replay operation discussed above may be implemented by the FPGA  906 . Specifically, the journal replay operations may be executed by one or more cores of the FPGA  906  (such as the one or more cores  512  discussed earlier herein with respect to FPGA  506 ). In conventional systems, journal replay operations may be executed by a host device; however, according to aspects of the present disclosure, journal replay operations can be easily offloaded into the computational storage device  404 , thereby freeing up computational resources (e.g., CPU, GPU, etc.) on the host device. 
     The file system driver  912  may prepare transactions in the journal  904  within memory of the file system driver  902  (e.g., DRAM). For example, the file system driver  912  can prepare one or more 4K pages that each contain one or more journal transactions. After the journal  904  is prepared by the file system driver  912 , the journal  904  may be transmitted to the computational storage device  903 . Data manipulations and computations based on the journal  904  may then be executed on the computational storage device  903 . For example, journal content (e.g., transactions) may be read by the FPGA  906  to perform data manipulation or computations. For example, the FPGA  906  may perform one or more read commands to execute functions or computations based on transactions in a section of the volume  908  that correspond to the journal  904 . Generally, methods and systems disclosed herein provide powerful techniques to offload data processing onto a computational storage device (e.g., computational storage device  903 ) that interacts with a host device (e.g., file system driver  912 ). 
       FIG.  10    illustrates an example process  1000  for offloading data processing into computational storage according to some aspects described herein. In examples, aspects of method  1000  may be performed by a system, such as system  200 ,  230 ,  260 ,  300 ,  400 ,  500 ,  600 ,  700 , or  800  discussed earlier herein with respect to  FIGS.  2 - 8   .  FIG.  11    illustrates an example system  1100  with data flows that correspond to aspects of the example method  1000  shown in  FIG.  10   . The example system  1100  may be similar to the example systems  200 ,  230 ,  260 ,  300 ,  400 ,  500 ,  600 ,  700 , or  800  discussed above with respect to  FIGS.  2 - 8    and may include, for example, an application  1110 , file system  1112 , io_uring  1150 , journaling subsystem  1154 , block layer  1114 , and computational storage device  1104 . In an example, storage device  1104  may include FPGA  1116 , journal  1126 , eBPF subsystem  1106 , file  1140 , xattr namespace  1142 . 
     Process  1000  begins at operation  1002 , where a request to offload data processing into computation storage is received. In an embodiment, the request may be transmitted by an application (e.g., any of application  310 ,  610 ,  710 ,  810 , and  1100  in  FIGS.  3 ,  6 ,  7 ,  8 , and  11   , respectively). For example, the application may transmit the request by defining a file identification (ID), an eBPF function name, or both. The request to offload data processing into computational storage may be prepared by an application programming interface (API). For example, the request to offload data may be implemented by placing the request into a submission queue of io_uring subsystem  1150 . In an embodiment, the request to offload data may be implemented by a dedicated system call (e.g., offload_computation(int fd, const char *function_name, . . . )) that is managed by a kernel-space file system (e.g., file system  1112 ). 
     The computational storage may correspond to one or more aspects of a storage device (e.g., the computational storage device of systems  200 ,  230 ,  260 ,  300 ,  400 ,  500 ,  600 , or  1100 ). The computational storage can include one of a field programmable gate array (FPGA), an infrastructure processing unit (IPU), a central processing unit (CPU), and a smart network interface controller (smartNIC). The computational storage can further include non-volatile memory or persistent memory (e.g., flash memory) with instructions stored therein. The computational storage can further include a plurality of cores. 
     At operation  1004 , one or more transactions are prepared to encapsulate the request received at operation  1002 . In an embodiment, a journaling subsystem (e.g., journaling subsystem for the journal  904  shown in  FIG.  9   , journaling subsystem  1150  in the system  1100  of  FIG.  11   ) may prepare the one or more transactions to encapsulate the request. 
     At operation  1006 , one or more write requests may be generated based on the one or more transactions prepared at operation  1004 . In an embodiment, a journaling subsystem (e.g., journaling subsystem for the journal  904  shown in  FIG.  9    and discussed in detail above) may generate the one or more write requests with the transactions prepared at operation  1004 . 
     At operation  1008 , the one or more transactions may be stored into one or more journals. For example, the one or more transactions may be stored into one or more journals (e.g., journal  904  shown in  FIG.  9   , journal  1126  in  FIG.  11   ) by the computational storage device (e.g., the computational storage device of systems  200 ,  230 ,  260 ,  300 ,  400 ,  500 ,  600 ,  900 , or  1100 ). In an embodiment, the one or more journals may be represented by a dedicated partition of persistent memory (e.g., NAND flash). Additionally, or alternatively, the one or more journals may be represented by a corresponding partition of non-volatile memory (NVM), storage class memory (SCM), byte-addressable memory, or DRAM memory. 
     At operation  1010 , a set of transactions may be extracted from the one or more journals into which the prepared transactions were stored at operation  1008 . In an example, FPGA logic  1116  of the computational storage device (e.g., the computational storage device of systems  200 ,  230 ,  260 ,  300 ,  400 ,  500 ,  600 ,  900 , or  1100 ) may retrieve the set of transactions from the one or more journals (e.g., journal  904  or  1126  shown in  FIGS.  9  and  11   , respectively). 
     At operation  1012 , a subset of the set of transactions extracted at operation  1010  may be received at an eBPF subsystem (e.g., eBPF subsystem  1106  of computational storage  1104  in  FIG.  11   ). In an example, the subset of the set of transactions received at operation  1012  corresponds to one or more computation requests. 
     At operation  1014 , information corresponding to one or more logical block addresses (LBAs) may be extracted from a file. In an embodiment, the eBPF subsystem  1106  may extract information about placement of the eBPF function and load the function logic to prepare the execution. The eBPF function can be represented by a special functional block implemented by the FPGA logic  1116 . Additionally, or alternatively, the eBPF function can be represented by an extended attribute stored into an LBA that needs to be loaded and represented by the FPGA logic  1116 . In an embodiment, the eBPF function can be represented by an item of eBPF maps. 
     At operation  1016 , the one or more computation requests may be performed on the one or more LBAs using the subset of the set of transactions. 
     At operation  1018 , an indication corresponding to the performed computation requests may be generated. In an example, the computational storage may return a status code of the executed operation to the block layer  1114 . The block layer  1114  may place a completion queue entry (CQE) in the completion queue of io_uring  1150 , in an embodiment. The application  1110  may then check the status of the offloaded data processing execution. 
       FIG.  11   , which is described in detail above with respect to the operations of the method  1000  of  FIG.  10   , shows an example of offloading data processing into computation storage when the eBPF function is already stored as an extended attribute of the file/folder. In such an example, the journal transaction needs to identify the location (LBA) of the eBPF function for execution of logic of data transformation.  FIG.  12    illustrates an example system  1200  for offloading data processing into computational storage in which an application  1210  prepares an eBPF function and sends the eBPF function to computational storage  1204  as part of a journal transaction. In the example of  FIG.  12   , use of an extended attribute of a file/folder is not necessary.  FIG.  12    shows many of the same data flows as in the example system  1100  of  FIG.  11   , and which correspond to aspects of the example method  1000  shown in  FIG.  10   . The example system  1200  may be similar to the example systems  200 ,  230 ,  260 ,  300 ,  400 ,  500 ,  600 ,  700 ,  800 , or  1100  discussed above with respect to  FIGS.  2 - 8  and  11    and may include, for example, application  1210 , libeBPF  1236 , file system  1212 , io_uring  1250 , journaling subsystem  1254 , block layer  1214 , and computational storage device  1204 . In an example, storage device  1204  may include FPGA  1216 , journal  1226 , eBPF subsystem  1206 , file  1240 , xattr namespace  1242 . 
       FIG.  13    shows an example system  1300  for offloading data processing into computational storage  1304  in which an eBPF function  1372  is registered and stored as an item of a map subsystem  1380 . In the system  1300  of  FIG.  13   , a journal transaction can simply identify the name of the eBPF function  1372 , and the identified name may be used to execute the eBPF function  1372  by accessing the eBPF function  1372  in the map subsystem  1380 .  FIG.  13    shows some of the same data flows as in the example systems  1100  and  1200  of  FIGS.  11  and  12   , respectively, and which correspond to certain aspects of the example method  1000  shown in  FIG.  10   . The example system  1300  may be similar to the example systems  200 ,  230 ,  260 ,  300 ,  400 ,  500 ,  600 ,  700 ,  800 ,  1100 , or  1200  discussed above with respect to  FIGS.  2 - 8 ,  11 , and  12    and may include, for example, application  1310 , file system  1312 , block layer  1314 , and computational storage device  1304 . In an example, storage device  1304  may include FPGA  1316 , eBPF subsystem  1306 , file  1340 , I/O requests queue  1360 , and map subsystem  1380 . The map subsystem  1380  may, for example, be comprised of LBA Range  1362 , event  1370 , and the eBPF function  1372 . 
       FIGS.  14 - 16    and the associated descriptions provide a discussion of a variety of operating environments in which aspects of the disclosure may be practiced. However, the devices and systems illustrated and discussed with respect to  FIGS.  14 - 16    are for purposes of example and illustration and are not limiting of a vast number of computing device configurations that may be utilized for practicing aspects of the disclosure, described herein. 
       FIG.  14    is a block diagram illustrating physical components (e.g., hardware) of a computing device  1400  with which aspects of the disclosure may be practiced. The computing device components described below may be suitable for the computing devices described above, including host devices and file systems  102 ,  202 ,  232 ,  262 ,  302 ,  402 ,  602 ,  712 ,  812 ,  1112 ,  1212 , and  1312  discussed with respect to  FIGS.  1 - 13   . In a basic configuration, the computing device  1400  may include at least one processing unit  1402  and a system memory  1404 . Depending on the configuration and type of computing device, the system memory  1404  may comprise, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories. 
     The system memory  1404  may include an operating system  1405  and one or more program modules  1406  suitable for running software application  1420 , such as one or more components supported by the systems described herein. The operating system  1405 , for example, may be suitable for controlling the operation of the computing device  1400 . 
     Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in  FIG.  14    by those components within a dashed line  1408 . The computing device  1400  may have additional features or functionality. For example, the computing device  1400  may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in  FIG.  14    by a removable storage device  1409  and a non-removable storage device  1410 . 
     As stated above, a number of program modules and data files may be stored in the system memory  1404 . While executing on the processing unit  1402 , the program modules  1406  (e.g., application  1420 ) may perform processes including, but not limited to, the aspects, as described herein. Other program modules that may be used in accordance with aspects of the present disclosure may include electronic mail and contacts applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided application programs, etc. 
     Furthermore, embodiments of the disclosure may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. For example, embodiments of the disclosure may be practiced via a system-on-a-chip (SOC) where each or many of the components illustrated in  FIG.  17    may be integrated onto a single integrated circuit. Such an SOC device may include one or more processing units, graphics units, communications units, system virtualization units and various application functionality all of which are integrated (or “burned”) onto the chip substrate as a single integrated circuit. When operating via an SOC, the functionality, described herein, with respect to the capability of client to switch protocols may be operated via application-specific logic integrated with other components of the computing device  1400  on the single integrated circuit (chip). Embodiments of the disclosure may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the disclosure may be practiced within a general-purpose computer or in any other circuits or systems. 
     The computing device  1400  may also have one or more input device(s)  1412  such as a keyboard, a mouse, a pen, a sound or voice input device, a touch or swipe input device, etc. The output device(s)  1414  such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used. The computing device  1700  may include one or more communication connections  1416  allowing communications with other computing devices  1450  or computational storage devices  1440 . Examples of suitable communication connections  1416  include, but are not limited to, radio frequency (RF) transmitter, receiver, and/or transceiver circuitry; universal serial bus (USB), parallel, and/or serial ports. The computational storage devices  1440  may be similar to the computational storage devices  104 ,  204 ,  234 ,  264 ,  304 ,  404 ,  504 ,  604 ,  1004 ,  1104 , and  1204  discussed with respect to  FIGS.  1 - 12   . 
     The term computer readable media as used herein may include computer storage media. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, or program modules. The system memory  1404 , the removable storage device  1409 , and the non-removable storage device  1410  are all computer storage media examples (e.g., memory storage). Computer storage media may include RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other article of manufacture which can be used to store information and which can be accessed by the computing device  1400 . Any such computer storage media may be part of the computing device  1400 . Computer storage media does not include a carrier wave or other propagated or modulated data signal. 
     Communication media may be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. 
       FIGS.  15 A and  15 B  illustrate a mobile computing device  1500 , for example, a mobile telephone, a smart phone, wearable computer (such as a smart watch), a tablet computer, a laptop computer, and the like, with which embodiments of the disclosure may be practiced. In some aspects, the client may be a mobile computing device. With reference to  FIG.  15 A , one aspect of a mobile computing device  1500  for implementing the aspects is illustrated. In a basic configuration, the mobile computing device  1500  is a handheld computer having both input elements and output elements. The mobile computing device  1500  typically includes a display  1505  and one or more input buttons  1510  that allow the user to enter information into the mobile computing device  1500 . The display  1505  of the mobile computing device  1500  may also function as an input device (e.g., a touch screen display). 
     If included, an optional side input element  1515  allows further user input. The side input element  1515  may be a rotary switch, a button, or any other type of manual input element. In alternative aspects, mobile computing device  1500  may incorporate more or less input elements. For example, the display  1505  may not be a touch screen in some embodiments. 
     In yet another alternative embodiment, the mobile computing device  1500  is a portable phone system, such as a cellular phone. The mobile computing device  1500  may also include an optional keypad  1535 . Optional keypad  1535  may be a physical keypad or a “soft” keypad generated on the touch screen display. 
     In various embodiments, the output elements include the display  1505  for showing a graphical user interface (GUI), a visual indicator  1520  (e.g., a light emitting diode), and/or an audio transducer  1525  (e.g., a speaker). In some aspects, the mobile computing device  1500  incorporates a vibration transducer for providing the user with tactile feedback. In yet another aspect, the mobile computing device  1500  incorporates input and/or output ports, such as an audio input (e.g., a microphone jack), an audio output (e.g., a headphone jack), and a video output (e.g., a HDMI port) for sending signals to or receiving signals from an external device. 
       FIG.  15 B  is a block diagram illustrating the architecture of one aspect of a mobile computing device. That is, the mobile computing device  1500  can incorporate a system (e.g., an architecture)  1502  to implement some aspects. In one embodiment, the system  1502  is implemented as a “smart phone” capable of running one or more applications (e.g., browser, e-mail, calendaring, contact managers, messaging clients, games, and media clients/players). In some aspects, the system  1502  is integrated as a computing device, such as an integrated personal digital assistant (PDA) and wireless phone. 
     One or more application programs  1566  may be loaded into the memory  1562  and run on or in association with the operating system  1564 . Examples of the application programs include phone dialer programs, e-mail programs, personal information management (PIM) programs, word processing programs, spreadsheet programs, Internet browser programs, messaging programs, and so forth. The system  1502  also includes a non-volatile storage area  1568  within the memory  1562 . The non-volatile storage area  1568  may be used to store persistent information that should not be lost if the system  1502  is powered down. The application programs  1566  may use and store information in the non-volatile storage area  1568 , such as e-mail or other messages used by an e-mail application, and the like. A synchronization application (not shown) also resides on the system  1502  and is programmed to interact with a corresponding synchronization application resident on a host computer to keep the information stored in the non-volatile storage area  1568  synchronized with corresponding information stored at the host computer. As should be appreciated, other applications may be loaded into the memory  1562  and run on the mobile computing device  1500  described herein (e.g., a signal identification component, a gaze tracker component, a shared computing component, etc.). 
     The system  1502  has a power supply  1570 , which may be implemented as one or more batteries. The power supply  1570  might further include an external power source, such as an AC adapter or a powered docking cradle that supplements or recharges the batteries. 
     The system  1502  may also include a radio interface layer  1572  that performs the function of transmitting and receiving radio frequency communications. The radio interface layer  1572  facilitates wireless connectivity between the system  1502  and the “outside world,” via a communications carrier or service provider. Transmissions to and from the radio interface layer  1572  are conducted under control of the operating system  1564 . In other words, communications received by the radio interface layer  1572  may be disseminated to the application programs  1566  via the operating system  1564 , and vice versa. 
     The visual indicator  1520  may be used to provide visual notifications, and/or an audio interface  1574  may be used for producing audible notifications via the audio transducer  1525 . In the illustrated embodiment, the visual indicator  1520  is a light emitting diode (LED) and the audio transducer  1525  is a speaker. These devices may be directly coupled to the power supply  1570  so that when activated, they remain on for a duration dictated by the notification mechanism even though the processor  1560  and/or special-purpose processor  1561  and other components might shut down for conserving battery power. The LED may be programmed to remain on indefinitely until the user takes action to indicate the powered-on status of the device. The audio interface  1574  is used to provide audible signals to and receive audible signals from the user. For example, in addition to being coupled to the audio transducer  1525 , the audio interface  1574  may also be coupled to a microphone to receive audible input, such as to facilitate a telephone conversation. In accordance with embodiments of the present disclosure, the microphone may also serve as an audio sensor to facilitate control of notifications, as will be described below. The system  1502  may further include a video interface  1576  that enables an operation of an on-board camera  1530  to record still images, video stream, and the like. 
     A mobile computing device  1500  implementing the system  1502  may have additional features or functionality. For example, the mobile computing device  1500  may also include additional data storage devices (removable and/or non-removable) such as, magnetic disks, optical disks, or tape. Such additional storage is illustrated in  FIG.  15 B  by the non-volatile storage area  1568 . 
     Data/information generated or captured by the mobile computing device  1500  and stored via the system  1502  may be stored locally on the mobile computing device  1500 , as described above, or the data may be stored on any number of storage media that may be accessed by the device via the radio interface layer  1572  or via a wired connection between the mobile computing device  1500  and a separate computing device associated with the mobile computing device  1500 , for example, a server computer in a distributed computing network, such as the Internet. As should be appreciated such data/information may be accessed via the mobile computing device  1500  via the radio interface layer  1572  or via a distributed computing network. Similarly, such data/information may be readily transferred between computing devices for storage and use according to well-known data/information transfer and storage means, including electronic mail and collaborative data/information sharing systems. 
       FIG.  16    illustrates an exemplary tablet computing device  1600  that may execute one or more aspects disclosed herein. In addition, the aspects and functionalities described herein may operate over distributed systems (e.g., cloud-based computing systems), where application functionality, memory, data storage and retrieval and various processing functions may be operated remotely from each other over a distributed computing network, such as the Internet or an intranet. User interfaces and information of various types may be displayed via on-board computing device displays or via remote display units associated with one or more computing devices. For example, user interfaces and information of various types may be displayed and interacted with on a wall surface onto which user interfaces and information of various types are projected. Interaction with the multitude of computing systems with which embodiments of the invention may be practiced include, keystroke entry, touch screen entry, voice or other audio entry, gesture entry where an associated computing device is equipped with detection (e.g., camera) functionality for capturing and interpreting user gestures for controlling the functionality of the computing device, and the like. 
     In accordance with at least one example of the present disclosure, a method is described. The method may include receiving a request to offload data processing into computational storage; preparing one or more transactions to encapsulate the request; generating one or more write requests, based on the one or more transactions; storing the one or more transactions into one or more journals; extracting a set of transactions from the one or more journals; receiving, at an eBPF subsystem, a subset of the set of transactions, the subset corresponding to one or more computation requests; extracting information from a file, the information corresponding to one or more logical block addresses (LBAs); performing the one or more computation requests on the one or more LBAs using the subset of the set of transactions; and generating an indication corresponding to the performed computation requests. 
     In accordance with at least one aspect of the above example, an application may transmit the request by defining one or more of (i) a file identification (ID) and (ii) an eBPF function name. 
     In accordance with at least one aspect of the above example, the application may receive the indication corresponding to the performed computation request. 
     In accordance with at least one aspect of the above example, the request to offload data may be implemented by a dedicated system call that is managed by a kernel-space file system. 
     In accordance with at least one aspect of the above example, the request may be prepared by an application programming interface (API). 
     In accordance with at least one aspect of the above example, the one or more journals may each be represented by a corresponding partition of one or more from the group comprising (i) persistent memory, (ii) non-volatile memory (NVM), (iii) storage class memory (SCM), (iv) byte-addressable memory, and (v) DRAM memory. 
     In accordance with at least one aspect of the above example, the method may further include, prior to preparing the one or more transactions to encapsulate the request, storing the request into a submission queue. 
     In accordance with at least one aspect of the above example, the one or more LBAs may comprise a range of LBAs, and the one or more computation requests may be performed on the range of LBAs. 
     In accordance with at least one aspect of the above example, the computational storage may invluce one or more cores of a field programmable gate array (FPGA). 
     In accordance with at least one example of the present disclosure, a system is described. In example, the system may include a first device comprising a computational component; a second device comprising a computational component; and memory storing instructions that, when executed by the computational component of at least one of the first device and the second device, causes the system to perform a set of operations. In examples, the set of operations may include receiving, via the computational component of the first device, a request to offload data processing into the second device; preparing, via the computational component of the first device, one or more transactions to encapsulate the request; generating, via the computational component of the first device, one or more write requests based on the one or more transactions; storing, via the computational component of the first device, the one or more transactions into one or more journals of the first device; extracting, via the computational component of the second device, a set of transactions from the one or more journals; receiving, via an eBPF subsystem of the second device, a subset of the set of transactions, the subset corresponding to one or more computation requests; extracting, via the computational component of the second device, information from a file, the information corresponding to one or more logical block addresses (LBAs); performing, via the computational component of the second device, the one or more computation requests on the one or more LBAs using the subset of the set of transactions; and generating, via the computational component of the second device, an indication corresponding to the performed computation requests. 
     In accordance with at least one aspect of the above example, the computational component of the first device may be a central processing unit (CPU), and the computational component of the second device may be a field programmable gate array (FPGA) comprising one or more cores. 
     In accordance with at least one aspect of the above example, the first device may include an application and the set of operations may include transmitting, via the application of the first device, the request by defining one or more of (i) a file identification (ID) and (ii) an eBPF function name. 
     In accordance with at least one aspect of the above example, the set of operations may include receiving, via the application of the first device, the indication corresponding to the performed computation request. 
     In accordance with at least one aspect of the above example, the request to offload data may be implemented by a dedicated system call that is managed by a kernel-space file system of the first device. 
     In accordance with at least one aspect of the above example, the request may be prepared by an application programming interface (API) of the first device. 
     In accordance with at least one aspect of the above example, the one or more journals may each be represented by a corresponding partition of one or more from the group comprising (i) persistent memory, (ii) non-volatile memory (NVM), (iii) storage class memory (SCM), (iv) byte-addressable memory, and (v) DRAM memory. 
     In accordance with at least one aspect of the above example, the set of operations may include, prior to preparing the one or more transactions to encapsulate the request, storing, via the computational component of the first device, the request into a submission queue. 
     In accordance with at least one aspect of the above example, the one or more LBAs may comprise a range of LBAs, and the one or more computation requests may be performed on the range of LBAs. 
     Aspects of the present disclosure, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to aspects of the disclosure. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. 
     The description and illustration of one or more aspects provided in this application are not intended to limit or restrict the scope of the disclosure as claimed in any way. The aspects, examples, and details provided in this application are considered sufficient to convey possession and enable others to make and use claimed aspects of the disclosure. The claimed disclosure should not be construed as being limited to any aspect, example, or detail provided in this application. Regardless of whether shown and described in combination or separately, the various features (both structural and methodological) are intended to be selectively included or omitted to produce an embodiment with a particular set of features. Having been provided with the description and illustration of the present application, one skilled in the art may envision variations, modifications, and alternate aspects falling within the spirit of the broader aspects of the general inventive concept embodied in this application that do not depart from the broader scope of the claimed disclosure.