Miracle-dz/UniFAR-FTUs huggingface dataset:
PMLDS: An LSM-Tree Direct Managed Storage for Key-Value Stores on Byte-Addressable Devices

Existing key-value stores (KVSs) based on log-structured merge-tree (LSM-tree) have been broadly deployed in practice to leverage characteristics of conventional block storage via file system, but lack effective exploitation for emerging byte-addressed persistent memory (PM). We reveal that these KVSs running upon existing PM-aware File systems cause inefficient PM I/O behaviors, including 1) numerous page faults, 2) I/O misaligned with cacheline, and 3) bandwidth wastage of concurrent I/O threads. To make full use of PM without major modification for existing LSM-based KVSs, this paper proposes PMLDS, a direct managed storage for LSM-tree-based KVSs directly running upon PM. PMLDS acts as a unified I/O layer to handle all requests from KVS to PM. PMLDS designs an LSM-tree-aware data layout to directly map the KVS’s persistent objects to the storage slots with fixed location and size, thus simplifying and replacing the file system’s functionality with a minor modification. To improve I/O efficiency, PMLDS further presents three key techniques: 1) pre-allocating reusable data slots to avoid page faults, 2) forcing cacheline-alignment for small requests, and 3) scheduling asynchronous I/O threads to harness PM’s limited parallelism. We implement PMLDS and evaluate it with popular RocksDB under a variety of workloads. The results show that compared to representative PM-aware file systems such as Ext4-DAX, XFS-DAX, NOVA, and WineFS, PMLDS improves the write performance of RocksDB by up to 2.1 × while reducing the read latency by 20%~50%.
EC-SpMM: Efficient Compilation of SpMM Kernel on GPUs

As deep neural networks (DNNs) become increasingly large and complicated, pruning techniques are proposed for lower memory footprint and more efficient inference. The most critical kernel to execute pruned sparse DNNs on GPUs is Sparse-dense Matrix Multiplication (SpMM). To maximize the performance of SpMM, despite the high-performance code generated from recent tensor compilers, they often take a long time for iteratively searching candidate configurations. Such a long time slows down the cycle of exploring better DNN architectures or pruning algorithms. In this paper, we propose EC-SpMM to efficiently generate high-performance SpMM kernels for sparse DNN inference. Based on the analysis of nonzero elements’ layout, the characterization of GPU architecture, and a rank-based cost model, EC-SpMM can effectively reduce the search space and eliminate possibly low-performance candidates. Experimental results show that EC-SpMM can reduce the compilation time by a factor of 35 ×, while the performance of generated SpMM kernels is comparable or even better, compared with the state-of-the-art sparse tensor compiling solution.
Scheduling Dependent Batching Tasks

We formulate and analyze the following problem of scheduling dependent batching tasks on a common resource. Each task is associated with an execution time window. Some can be executed simultaneously in batch, while others need exclusively use of the resource. A task may depend on a set of tasks such that it becomes executable only if all its ancestor tasks are completed. We look for a task scheduling policy maximizing the system reward. We investigate both the offline and online settings by focusing on a typical scenario where the task dependency forms a tree or a forest. In both settings, we formally establish the hardness of the scheduling problem by showing that the offline scheduling is NP-hard and the online counterpart admits no scheduling policy with finite competitive ratio. We then develop approximation scheduling algorithms for both cases with deterministic worst-case performance guarantee in terms of system utility. We further conduct numeric experiments to evaluate our algorithms under a variety of parameter settings to demonstrate the effectiveness of our scheduling algorithms.
Solving the Storage Location Assignment Problem Using Reinforcement Learning

In this work, we deal with the Storage Location Assignment Problem, often referred to as the SLAP, in an E-commerce Distribution Center (EDC). With E-commerce steadily increasing in popularity over the past decades, it has become a key part of the logistics industry. Due to the direct link with the customer, EDC's are forced into a significantly more complex and dynamic order picking process compared to conventional Bulk Distribution Centers. As a result of these challenges, many traditional approaches such as genetic algorithms and rule-based methods reach only suboptimal solutions. We propose the use of Reinforcement Learning (RL) to solve the SLAP, leading to a solution that adapts to dynamically changing environment parameters during runtime. For this purpose, we define a model that transforms the SLAP into a sequential decision making problem. We validate this novel approach by training a state-of-the-art RL algorithm within this model and comparing its results with a benchmark genetic algorithm approach. We conclude that the RL algorithm achieves promising results, surpassing benchmark performance and nearing optimal performance in a small-scale warehouse environment.
SEECHIP: A Scalable and Energy-Efficient Chiplet-based GPU Architecture Using Photonic Links

The continuous increase in GPU performance benefits a wide range of high-performance computing (HPC) applications. Slower growth of transistor density and limited size of chip die are now posing significant challenges to scale GPUs. The chiplet technology provides a potential solution to surpass these limitations. However, the performance of these chiplet-based GPUs is often constrained by the metallic-based interconnects between the chiplets. Emerging technologies such as photonic interconnect can overcome the limitations of metallic interconnects, offering several superior properties, such as high bandwidth density and low energy consumption. In this paper, we propose SEECHIP: a Scalable and Energy-Efficient CHIPlet-based GPU architecture using photonic links. SEECHIP introduces a novel photonic inter-chiplet network that supports both unicast and broadcast communication, providing the same transmission bandwidth at both the sending and receiving ends. In addition, we propose a tailored hierarchical memory architecture, which is more suitable for the parallelization of general-purpose HPC applications. Simulation results using 14 benchmarks show that SEECHIP can achieve and reduction in execution time and energy consumption, respectively, as compared to other GPUs with metallic or photonic interconnects. Simulation results also show that SEECHIP has good scalability compared with the other GPUs.
DiffLex: A High-Performance, Memory-Efficient and NUMA-Aware Learned Index using Differentiated Management

Learned indexes that utilize machine learning models can offer significant performance advantages over traditional indexes. However, existing learned indexes suffer from space-performance tradeoffs and they cannot scale well in multiple NUMA-nodes machines. These issues limit the development of learned indexes in production environments. In this paper, we propose DiffLex, a high-performance, memory-efficient and NUMA-aware learned index. The core idea of DiffLex is to differentiate key management based on hotness. To achieve high performance, DiffLex stores newly inserted keys in sparse deltas and frequently accessed keys in a sparse hot cache. For cold keys that take up most of the storage space, however, DiffLex stores them in dense arrays to save memory costs. DiffLex also makes sparse deltas and hot cache NUMA-aware by partitioning sparse deltas and replicating hot cache across different NUMA nodes. Our evaluation shows that DiffLex outperforms the state-of-the-art ALEX by 3.88x and 1.82x for insert and search operations, respectively, while maintaining a small index size.
DrivingVibe: Enhancing VR Driving Experience using Inertia-based Vibrotactile Feedback around the Head

We present DrivingVibe, which explores vibrotactile feedback designs around the head to enhance VR driving motion experiences. We propose two approaches that use a 360-degree vibrotactile headband: 1) mirroring and 2) 3D inertia-based. The mirroring approach extends the vibrotactile patterns of handheld controllers to actuate the entire headband uniformly. The 3D inertia-based approach uses the acceleration telemetry data that driving games/simulators export to motion platforms to generate directional vibration patterns, including: i) centrifugal forces, ii) horizontal acceleration/deceleration, and iii) vertical motion due to rough terrain. The two approaches are complementary as the mirroring approach supports all driving games because it does not require telemetry data, while the 3D inertia-based approach provides higher feedback fidelity for games that provide such data. We conducted a 24-person user experience evaluation in both passive passenger mode and active driving mode. Study results showed that both DrivingVibe designs significantly improved realism, immersion, and enjoyment (p<.01) with large effect sizes for the VR driving experiences. For overall preference, 88% (21/24) of participants preferred DrivingVibe, with a 2:1 preference for 3D inertia-based vs. mirroring designs (14 vs. 7 participants). For immersion and enjoyment, 96% (23/24) of participants preferred DrivingVibe, with nearly a 3:1 preference (17 vs. 6 participants) for the 3D inertia-based design.
The Loop and Reasons to Break It: Investigating Infinite Scrolling Behaviour in Social Media Applications and Reasons to Stop

Today's social media (SM) platforms are toolkits consisting of features with different use cases, some strongly related to habitual and regretful use. Especially Infinite Scrolling (IS) has been reported to make users feel like they are being caught in a loop, regretfully elongating SM sessions. We investigated and defined this loop while unveiling the processes that make users break it. Based on a one-week-long field study (N=46), we unfolded and categorized general reasons for leaving social media and related those to IS. In light of our findings, we argue that SM interventions should not only focus on the app but incorporate the user's context, as most reasons to break SM sessions were not related to the app but the user's general context. Our findings and prior work also indicate the coexistence of multiple loops, which we define as inner (intra-session) loops surrounded by an outer (habitual) loop.
The Diversity-Hire Narrative in CS: Sources, Impacts, and Mitigation Strategies

Background: The goal of affirmative action programs (AAPs) is to address the underrepresentation of people from historically underrepresented groups (HUGs) in the workforce. People who identify as women, Black or African American, Hispanic or Latinx/a/o/*, Native American, Native Alaskan, Native Hawai’ian, and/or Pacific Islander are considered to be a part of HUGs in computing. AAPs can unintentionally signal that a person from a HUG was selected for a position based on their gender or race/ethnicity rather than their merit [2, 3, 5, 6]. We call this signal the diversity-hire narrative. In computing, prior work has found that women hear the diversity-hire narrative during their computer science (CS) internships [4], but women’s experiences surrounding the narrative have not been thoroughly explored. Objectives: The first objective of our work is to explore from whom undergraduate women in CS hear the diversity-hire narrative. The second objective is to understand how the diversity-hire narrative impacts undergraduate women in CS. The third objective is to understand how undergraduate women in CS mitigate the possible impacts of the diversity-hire narrative. Methods: We conducted 11 semi-structured interviews with third and fourth year undergraduate CS students who self-identified as women. Our interview questions centered around students’ experiences in their CS degree and their experiences applying for internships. The interviews also specifically asked about hearing the diversity-hire narrative. Our qualitative analysis used descriptive and values coding [1, 7]. From this coding process, we developed an initial set of findings. Results: Participants reported hearing the diversity-hire narrative from peers, family members, and online comments. Our work illustrates how undergraduate women in CS appear to be affected by hearing the diversity-hire narrative. For example, we interpret that some participants internalized aspects of the narrative: one participant reported being concerned that their success was due to their gender, despite having described extensive preparation for the interview. We further found that participants appeared to mitigate the impact of the diversity-hire narrative by stating that their peers who endorsed the diversity-hire narrative did so out of jealousy, that AAPs are needed to fix social inequities, and that only qualified candidates are hired. Implications: Our research expands our understanding of the experiences that may negatively impact people from HUGs in computing. Our findings show ways that people can mitigate the impacts of the diversity-hire narrative. The results of our research can help educators be better prepared to support students. For example, our results inform educators on where students may hear the diversity-hire narrative and how they can help challenge the diversity-hire narrative.
RBC: A bandwidth controller to reduce write-stalls and tail latency

We present RBC (Request Bandwidth Controller for LSM-tree), a controller that manages LSM-tree read and write operations and optimizes the requests for flash-based storage devices. RBC minimizes the number of write-stalls and reduces tail latency by precisely measuring the requests for solid-state disk bandwidth from various components within the LSM-tree. We have successfully implemented RBC on the state-of-the-art LSM-tree storage system, RocksDB. The experimental results show that using YSCB to evaluate RBC leads to a significant decrease in write-stalls and tail latency. Additionally, RBC provides higher write throughput (up to 1.52x) and high read throughput (up to 1.96x), while reducing read and write amplification by 55% and 37%, respectively. Furthermore, we observed a reduction of approximately 50% in tail latency with the use of RBC.
Removal of Ni2+ and Zn2+ from groundwater by adsorption onto fishbone and hydroxyapatite: Effect of salinity

Ni2+- and Zn2+-contaminated groundwater in the coastal regions is a serious threat to water security in industrial areas. Apatite-like material is an excellent sorbent for heavy metals; however the effect of salinity on the Ni2+ and Zn2+ adsorption onto fishbone and synthesized hydroxyapatite (HAP) has not been investigated. This study investigates the effect of salinity on the single and binary adsorption of Ni2+ and Zn2+ onto apatite-like materials. The experiments were conducted in batch reactor for 24 h at 1:40 solid-to-liquid ratios (wt/wt), 25°C, and pH 5. Freundlich, Langmuir, and Dubinin–Radushkevich models fit well with the single-adsorption data. The adsorption isotherms were nonlinear (NF = 0.350–0.710). The maximum adsorption capacities (qmL) of the Ni2+ and Zn2+ onto HAP were higher than those onto the fishbone, attributed to the higher Brunauer–Emmett–Teller surface area (ABET) and cation exchange capacity. In the binary adsorption, adsorption capacities of the adsorbents were less than those in the single-solute system due to the competition between Ni2+ and Zn2+. Salinity affected the single and binary adsorption by decreasing the adsorption capacities of the adsorbents. In a binary adsorption system, the selectivity of Zn2+ was less than that of Ni2+ for both fishbone and HAP at 0‰ and 30‰ salinity, respectively. Binary adsorption models, such as the Murali–Aylmore (M–A) model, competitive Langmuir model (CLM), P-factor model, and ideal-adsorbed solution theory coupled with the Freundlich (IAST-Freundlich) model were used; of these, the M–A model provided the best prediction for the binary system.
How does V1 population activity inform perceptual certainty?

Neural population activity in sensory cortex informs our perceptual interpretation of the environment. Oftentimes, this population activity will support multiple alternative interpretations. The larger the set of plausible alternatives, the more uncertain the selected perceptual interpretation. We test the hypothesis that the reliability of perceptual interpretations can be revealed through simple transformations of sensory population activity. We recorded V1 population activity in fixating macaques while presenting oriented stimuli under different levels of nuisance variability and signal strength. We developed a decoding procedure to infer from V1 activity the most likely stimulus orientation as well as the certainty of this estimate. Our analysis shows that response magnitude, response dispersion, and variability in response gain all offer useful proxies for orientation certainty. Of these three metrics, the last one has the strongest association with the decoder’s uncertainty estimates. These results clarify that the nature of neural population activity in sensory cortex provides downstream circuits with multiple options to assess the reliability of perceptual interpretations.
Genetic manipulation of an Ixodes scapularis cell line

Although genetic manipulation is one of the hallmarks in model organisms, its applicability to non-model species has remained difficult due to our limited understanding of their fundamental biology. For instance, manipulation of a cell line originated from the blacklegged tick Ixodes scapularis, an arthropod that serves as a vector of several human pathogens, has yet to be established. Here, we demonstrate the successful genetic modification of the commonly used tick ISE6 line through ectopic expression and clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) genome editing. We performed ectopic expression using nucleofection and attained CRISPR-Cas9 editing via homology dependent recombination. Targeting the E3 ubiquitin ligase X-linked inhibitor of apoptosis (xiap) and its substrate p47 led to alteration in molecular signaling within the immune deficiency (IMD) network and increased infection of the rickettsial agent Anaplasma phagocytophilum in I. scapularis ISE6 cells. Collectively, our findings complement techniques for genetic engineering of ticks in vivo and aid in circumventing the long-life cycle of I. scapularis, of which limits efficient and scalable molecular genetic screens. Importance Genetic engineering in arachnids has lagged compared to insects, largely because of substantial differences in their biology. This study unveils the implementation of ectopic expression and CRISPR-Cas9 gene editing in a tick cell line. We introduced fluorescently tagged proteins in ISE6 cells and edited its genome via homology dependent recombination. We ablated the expression of xiap and p47, two signaling molecules present in the immune deficiency (IMD) pathway of I. scapularis. Impairment of the tick IMD pathway, an analogous network of the tumor necrosis factor receptor in mammals, led to enhanced infection of the rickettsial agent A. phagocytophilum. Altogether, our findings provide a critical technical resource to the scientific community to enable a deeper understanding of biological circuits in the blacklegged tick Ixodes scapularis.
OxLDL/LOX-1 Mediated Sex, Age, and Cell Dependent Alterations in Mouse Thoracic Aortic Vascular Reactivity

Elevated oxidized low-density lipoprotein (oxLDL) is a risk factor and component that worsens cardiovascular disease states. OxLDL can elicit its detrimental action, via lectin-like oxLDL receptor 1 (LOX-1) and has been shown to disrupt vascular function. In this study, we determined whether oxLDL, via LOX-1, alters aortic vascular reactivity and determined if sex differences exist. Thoracic aortic endothelium-intact or -denuded ring segments were isolated from intact C57BL/6J female and male mice and incubated with oxLDL ex vivo (50ug/dL; 2h). Using wire myography, cumulative concentration-response curves to phenylephrine (PE) were generated to determine contractile responses. From these curves, the EC50 was determined and used to contract rings to assess acetylcholine (ACh) dependent relaxation. Calculated aortic stiffness and remodeling, as well as mRNA expression of vasoactive and pro-inflammatory mediators were assessed. BI-0115 (10μM; selective LOX-1 inhibitor) was used to determine LOX-1 dependence. We observed differential sex, age, endothelial cell, and LOX-1 dependent alterations to the efficacy of PE-induced contractile responses and ACh-mediated vasorelaxation in the thoracic aortic rings following oxLDL exposure. Additionally, we observed a distinct sex and age effect on thoracic aortic stiffness following exposure to oxLDL. There was also a sex effect on calculated vessel diameter, as well as an age effect on oxLDL-mediated inward remodeling that was LOX-1 dependent. Thus, LOX-1 inhibition and the resulting attenuation of oxLDL/endothelial-mediated alterations in aortic function suggests that there are differential sex differences in the role of oxLDL/LOX-1 in the thoracic aorta of male and female mice. NEW & NOTEWORTHY We investigated the effects of oxidized low-density lipoprotein (oxLDL) via the LOX-1 receptor on murine thoracic aortic vasoreactivity, stiffness, and remodeling across age and sex. Acute exposure to oxLDL led to altered vasoreactivity, endothelial dysfunction, and changes in aortic stiffness and remodeling. These effects were in-part age, sex, endothelial, and LOX-1 dependent. This study reveals potential complex interactions in oxLDL/LOX-1-mediated vascular responses that could serve as potential therapeutic intervention for vascular diseases such as atherosclerosis.