Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing Job Microarchitectural Profiles at Scale: Dataset and Analysis

Understanding the microarchitectural resource characteristics of datacenter jobs has become increasingly critical to guarantee the performance of jobs while improving resource utilization. Prior work studied the resource characteristics of datacenter jobs at the OS level, little reveals the deep and detailed characteristics at the microarchitecture level due to the lack of related open traces. In this paper, we provide a new open trace, AMTrace (Alibaba Microarchitecture Trace) 1, which is profiled from 8,577 high-end physical hosts from Alibaba’s datacenter by a hardware/software co-design monitoring method. AMTrace provides the microarchitectural metrics of 9.8 × 105 Linux containers with ”Per-Container-Per-Logic CPU” granularity. Different from existing open traces, AMTrace provides a new perspective to analyze the microarchitectural resource characteristics of datacenter jobs. Based on AMTrace, we first reveal the uneven resource usage of jobs among multiple logic CPUs. Then, we analyze the impact of resource contention of CPU and memory bandwidth on job performance. Finally, we analyze the job performance under different CPU provisioning modes from microarchitecture perspective. These analyses lead to constructive insights for datacenter resource management and optimization. Furthermore, we discuss possible research opportunities on AMTrace and we believe that AMTrace will inspire more exciting research on microarchitecture and resource management.
Repair-Optimal Data Placement for Locally Repairable Codes with Optimal Minimum Hamming Distance

Modern clustered storage systems increasingly adopt erasure coding to realize reliable data storage at low storage redundancy. Locally Repairable Codes (LRC) are a family of practical erasure codes with high repair efficiency. Among various LRC constructions, Optimal-LRC is a recently proposed LRC approach that achieves the optimal Minimum Hamming Distance with low theoretical repair costs. In this paper, we consider the repair performance of Optimal-LRC in clustered storage systems. We show that the conventional flat data placement and random data placement incur substantial cross-cluster repair traffic, which impairs the repair performance. To this end, we design an optimal data placement scheme that provably minimizes the cross-cluster repair traffic, by carefully placing each group of blocks in Optimal-LRC into a minimum number of clusters subject to single-cluster fault tolerance. We implement our optimal data placement scheme on a key-value store prototype atop Memcached, and show via LAN testbed experiments that the optimal data placement significantly improves the repair performance compared to the conventional data placements.
Surface emitting multi-wavelength array of single frequency quantum cascade lasers

We present a surface emitting laser array design based on distributed Bragg reflectors together with a second order extractor, providing a deterministic mode selection mechanism. The technology is implemented as a buried heterostructure compatible with continuous-wave operation and low dissipation. A proof of principle featured 10 regularly spaced single mode devices with a coverage of 175 cm−1 in the 8–10 μm wavelength range and milliwatt output powers with a far-field pattern full-width half-maximum of 8.3° in the longitudinal direction.
A Dynamic and Recoverable BMT Scheme for Secure Non-Volatile Memory

Data security is a key issue that non-volatile memory (NVM) system designers must consider. However, this is challenging because implementing security mechanisms such as bonsai merkle tree (BMT) in NVM needs to ensure crash recovery due to the non-volatile property of NVM. Existing schemes fail to efficiently guarantee the atomic BMT root update and instant system recovery required for BMT crash recovery, resulting in large write traffic and performance overhead. In this paper, we propose DR-TREE, a dynamic and recoverable BMT scheme for secure NVM, which reduces the update overhead of BMT root and achieves fast crash recovery with low write traffic. DR-TREE dynamically builds BMT and adjusts the updated BMT levels according to memory write requests, thus reducing unnecessary update overhead of BMT root. Next, based on the locality of memory write requests, DR-TREE merges repeated BMT updates, further decreasing the update overhead of BMT root. Moreover, DR-TREE achieves fast crash recovery with extremely low write traffic by delaying the partial recovery process. Experiments show that compared to the state-of-the-art design, DR-TREE improves the performance by 44.6%, decreases write traffic by 78.2% and achieves the system recovery in 5ms.
TileSpMSpV: A Tiled Algorithm for Sparse Matrix-Sparse Vector Multiplication on GPUs

Sparse matrix-sparse vector multiplication (SpMSpV) is an important primitive for graph algorithms and machine learning applications. The sparsity of the input and output vectors makes its floating point efficiency in general lower than sparse matrix-vector multiplication (SpMV) and sparse matrix-matrix multiplication (SpGEMM). Existing parallel SpMSpV methods focused on various row- and column-wise storage formats and merging operations. However, the data locality and sparsity pattern of the input matrix and vector are largely ignored. We in this paper propose TileSpMSpV, a tiled algorithm for accelerating SpMSpV on GPUs. Firstly, tile-wise storage structures are developed for fast positioning a group of nonzeros in matrix and vectors. Then, we develop the TileSpMSpV algorithm on top of the storage structures. In addition, to accelerate directional optimization breadth-first search (BFS) by using TileSpMSpV, we propose a TileBFS algorithm including three kernels called Push-CSC, Push-CSR and Pull-CSC. In the experiments running on a high-end NVIDIA GPU and using 2757 sparse matrices, the TileSpMSpV algorithm outperforms TileSpMV, cuSPARSE and CombBLAS by a factor of on average 1.83, 17.18 and 17.20 (up to 7.68, 1050.02 and 235.90), respectively. Moreover, our TileBFS algorithm outperforms Gunrock and GSwitch by a factor of on average 2.88 and 4.52 (up to 21.35 and 1000.85), respectively.
The 150 AU structure of the radio continuum and the ammonia bipolar outflow in CRL 618

We present high angular resolution (0″.096×0″.066) observations of the radio continuum emission at 23 GHz and of the (2, 2) line of NH 3 toward the protoplanetary nebula CRL 618. The integrated flux density in the continuum indicates that the source has not experienced any systematic increase since 1980. The continuum emission is elongated in the east-west direction and shows an elliptical shell structure, reminiscent of an extremely young planetary nebula. While the southern part of the elliptical shell is rather uniform, the northern portion shows clumpy structure. The walls of the shell are unresolved by our 0″. 066 beam
Classification Comparison of Activity Distraction Detection in Car Driving

Currently, development of safety in driving is advancing. Technology in safety in driving can be found in cars. A higher safety precautions is expected with lots of technology implemented in the car to minimize road accidents. However, most of the safety detection is built for the car and the external environment. Meanwhile, the human factor can also have determining role in safety driving. Safety driving to detect driver behavior is still not widely implemented. Therefore, a way is needed to be able to detect safe driving with the driver as the object. Furthermore, the development of an image-based safety driving system is a challenge so that the goal of driver safety can be achieved. Thus, there is a need to develop a system that could detect driver activity during driving. As an initial exploration, this study aims to compare four methods of machine learning and deep learning with the task of classifying activity distraction in car driving. Four algorithm used in this study are Random Forest, KNN, SVM, and CNN. In addition, this study also compares algorithms in terms of algorithm training speed since such detection needs fast recognition. The results of this study shows that Random Forest outperform all other algorithm for activity distraction with accuracy of 98.85%.
A User-Based Bike Return Algorithm for Docked Bike Sharing Systems

Recently, the development of Internet connection, intelligence, and sharing in the bicycle industry has assisted bike sharing systems (BSS’s) in establishing a connection between public transport hubs. In this paper, we propose a novel user-based bike return (UBR) algorithm for docked BSS’s which leverages a dynamic price adjustment mechanism so that the system is able to rebalance the number of lent and returned bikes by itself at different docks nearby. The proposed scheme motivates users to return their bikes to other underflow docks close-by their target destinations through a cheaper plan to compensate the shortage in them. Consequentially, the bike sharing system is able to achieve dynamic self-balance and the operational cost of the entire system for operators is reduced while the satisfaction of users is significantly increased. The simulations are conducted using real traces, called Citi Bike, and the results demonstrate that the proposed UBR achieves its design goals.
Efficient Support of the Scan Vector Model for RISC-V Vector Extension

RISC-V vector extension (RVV) provides wide vector registers, which is applicable for workloads with high data-level parallelism such as machine learning or cloud computing. However, it is not easy for developers to fully utilize the underlying performance of a new architecture. Hence, abstractions such as primitives or software frameworks could be employed to ease this burden. Scan, also known as all-prefix-sum, is a common building block for many parallel algorithms. Blelloch presented an algorithmic model called the scan vector model, which uses scan operations as primitives, and demonstrates that a broad range of applications and algorithms can be implemented by them. In our work, we present an efficient support of the scan vector model for RVV. With this support, parallel algorithms can be developed upon those primitives without knowing the details of RVV while gaining the performance that RVV provides. In addition, we provide an optimization scheme related to the length multiplier feature of RVV, which can further improve the utilization of the vector register files. The experiment shows that our support of scan and segmented scan for RVV can achieve 2.85x and 4.29x speedup, respectively, compared to the sequential implementation. With further optimization using the length multiplier of RVV, we can improve the previous result to 21.93x and 15.09x speedup.
Simmer: Rate proportional scheduling to reduce packet drops in vGPU based NF chains

Network Function Virtualization (NFV) paradigm offers flexibility, cost benefits, and ease of deployment by decoupling network function from hardware middleboxes. The service function chains (SFC) deployed using the NFV platform require efficient sharing of resources among various network functions in the chain. Graphics Processing Units (GPUs) have been used to improve various network functions’ performance. However, sharing a single GPU among multiple virtualized network functions (virtual machines) in a service function chain has been challenging due to their proprietary hardware and software stack. Earlier GPU architectures had a limitation: a single physical GPU can only be allocated to one virtual machine (VM) and cannot be shared among multiple VMs. The newer GPUs are virtualization-aware (hardware-assisted virtualization) and allow multiple virtual machines to share a single physical GPU. Although virtualization-aware, these GPUs still lack support for custom scheduling policies and do not expose the preemption control to users. When network functions (hosted within virtual machines) with different processing requirements share the same GPU, virtualization-aware GPUs’ default round-robin scheduling mechanism proves to be inefficient, resulting in packet drops and lower throughput. This paper presents Simmer, an efficient mechanism for scheduling a network function service chain on virtualization-aware GPUs. Our scheduling solution considers the processing requirement of NFs in a GPU-based SFC, thus improving overall throughput by up to 29% and reducing the packet drop to zero compared to vanilla setup.
Spread: Decentralized Model Aggregation for Scalable Federated Learning

Federated learning (FL) is a new distributed machine learning paradigm that enables machine learning on edge devices. One unique feature of FL is that edge devices belong to individuals; and since they are not “owned” by the FL coordinator, but can be “federated” instead, there can potentially be a huge number of edge devices. In the current distributed ML architecture, the parameter server (PS) architecture, model aggregation is centralized. When facing a large number of edge devices, the centralized model aggregation becomes the bottleneck and fundamentally restricts system scalability. In this paper, we present Spread to decentralize model aggregation. Spread is a tiered architecture where nodes are organized into clusters so that model aggregation can be offloaded to certain edge devices. We design a Spread-based FL system: it employs a new algorithm for cluster construction and an adaptive algorithm that regulates, in runtime, inter-cluster model training and intra-cluster model training. We present an implementation of a functional system by extending the Federated Learning system. Our evaluation shows that Spread can resolve the bottleneck of centralized model aggregation. Spread yields an 8.05 × and a 5.58 × model training speedup as compared to existing FL systems supported by the PS and allReduce architecture.
Atos: A Task-Parallel GPU Scheduler for Graph Analytics

We present Atos, a task-parallel GPU dynamic scheduling framework that is especially targeted at dynamic irregular applications. Compared to the dominant Bulk Synchronous Parallel (BSP) frameworks, Atos exposes additional concurrency by supporting task-parallel formulations of applications with relaxed dependencies, achieving higher GPU utilization, which is particularly significant for problems with concurrency bottlenecks. Atos also offers implicit task-parallel load balancing in addition to data-parallel load balancing, providing users the flexibility to balance between them to achieve optimal performance. Finally, Atos allows users to adapt to different use cases by controlling the kernel strategy and task-parallel granularity. We demonstrate that each of these controls is important in practice. We evaluate and analyze the performance of Atos vs. BSP on three applications: breadth-first search, PageRank, and graph coloring. Atos implementations achieve geomean speedups of 3.44x, 2.1x, and 2.77x and peak speedups of 12.8x, 3.2x, and 9.08x across three case studies, compared to a state-of-the-art BSP GPU implementation. Beyond simply quantifying the speedup, we extensively analyze the reasons behind each speedup. This deeper understanding allows us to derive general guidelines for how to select the optimal Atos configuration for different applications. Finally, our analysis provides insights for future dynamic scheduling framework designs.
Improving Stress Detection Using Weighted Score-Level Fusion of Multiple Sensor

Work-related stress is now a widespread issue in our modern life. Consequently, stress management is crucial. Automated stress detection is one of the innovations in helping people manage their well-being better by providing information about their stress levels. The advancement of sensor technology and artificial intelligence has made this task easier. The assessment of stress levels by using a variety of sensors from a smartwatch and machine learning algorithms has been very popular in recent years. The use of multiple sensor data enables richer information to train the machine learning algorithm so that the trained model can be more robust. However, if we use a feature-level fusion, since it is the most popular fusion strategy, generally, each sensor data will have the same significance. In fact, stress is personal and each subject can have a different reaction to the stress so that a particular sensor may be an effective stress indicator for some subjects but it might not be for others. Therefore, we propose a personalized stress detection system based on a weighted score-level multiple sensor fusion strategy. For each individual, this strategy gives different weights to each sensor based on the performance of the sensor on the individual’s data. The experiment results show that both feature-level and weighted score-level fusion models obtained better performance than models from the individual sensor strategy. The weighted score-level fusion strategy achieved a better performance than the feature-level strategy with accuracy, precision, recall, and F1-measure of 0.931, 0.824, 0.939, and 0.868, respectively.
Regularizing Sparse and Imbalanced Communications for Voxel-based Brain Simulations on Supercomputers

Inter-process communications form a performance bottleneck for large-scale brain simulations. The sparse and imbalanced communication patterns of human brain make it particularly challenging to design a communication system for supporting large-scale brain simulations. In this paper, we tackle the communication challenges posed by large-scale brain simulations with sparse and imbalanced communication patterns. We design a virtual communication topology with a merge and forward algorithm that exploits the sparsity to regularize inter-process communications. To balance the communication loads of different processes, we formulate voxel partition in brain simulations as a k-way graph partition problem and propose a constrained deterministic greedy algorithm to solve the problem effectively. We conducted extensive simulation experiments for evaluating the performance of the proposed communication scheme and found that the proposed method may significantly reduce communication overheads and shorten simulation time for large-scale brain models.
Enabling Latency-Sensitive DNN Inference via Joint Optimization of Model Surgery and Resource Allocation in Heterogeneous Edge

Nowadays, edge computing is widely adopted to resolve the emerging deep neural networks (DNNs)-driven intelligence scenarios with the requirement of low-latency and high-accuracy, which includes heterogeneous end devices and DNNs. In such scenarios, the influx of data and computation into a shared edge server incurs prohibitive latency. Thus, we exploit the advantage of Multi-exit DNNs (ME-DNNs) that tasks can exit early at appropriate depths to save inference time. However, naively using ME-DNNs in the heterogeneous edge still fails to deliver fast inference due to improper model surgery and resource allocation. In this paper, we propose an Acceleration scheme for Inference based on ME-DNNs with Adaptive model surgery and resource allocation (AIMA) to accelerate DNN inferences. We model this problem as a mixed-integer programming problem that involves jointly optimizing model surgery and resource allocation to minimize the task completion time. We first determine the optimal resource allocation policy with a given model surgery decision profile, and then the model surgery decision-making is modeled as a weighted congestion game. We prove the existence of the Nash equilibrium and propose a decentralized algorithm. Extensive experimental results show that AIMA significantly outperforms the state-of-the-art methods, achieving up to 6.01 × speedup.
Ethanolaminephosphate cytidyltransferase is essential for survival, lipid homeostasis and stress tolerance in Leishmania major

Glycerophospholipids including phosphatidylethanolamine (PE) and phosphatidylcholine (PC) are vital components of biological membranes. Trypanosomatid parasites of the genus Leishmania can acquire PE and PC via de novo synthesis and the uptake/remodeling of host lipids. In this study, we investigated the ethanolaminephosphate cytidyltransferase (EPCT) in Leishmania major, which is the causative agent for cutaneous leishmaniasis. EPCT is a key enzyme in the ethanolamine branch of the Kennedy pathway which is responsible for the de novo synthesis of PE. Our results demonstrate that L. major EPCT is a cytosolic protein capable of catalyzing the formation of CDP-ethanolamine from ethanolamine-phosphate and cytidine triphosphate. Genetic manipulation experiments indicate that EPCT is essential in both the promastigote and amastigote stages of L. major as the chromosomal null mutants cannot survive without the episomal expression of EPCT. This differs from our previous findings on the choline branch of the Kennedy pathway (responsible for PC synthesis) which is required only in promastigotes but not amastigotes. While episomal EPCT expression does not affect promastigote proliferation under normal conditions, it leads to reduced production of ethanolamine plasmalogen or plasmenylethanolamine, the dominant PE subtype in Leishmania. In addition, parasites with epsiomal EPCT exhibit heightened sensitivity to acidic pH and starvation stress, and significant reduction in virulence. In summary, our investigation demonstrates that proper regulation of EPCT expression is crucial for PE synthesis, stress response, and survival of Leishmania parasites throughout their life cycle. AUTHOR SUMMARY In nature, Leishmania parasites alternate between fast replicating, extracellular promastigotes in sand fly gut and slow growing, intracellular amastigotes in macrophages. Previous studies suggest that promastigotes acquire most of their lipids via de novo synthesis whereas amastigotes rely on the uptake and remodeling of host lipids. Here we investigated the function of ethanolaminephosphate cytidyltransferase (EPCT) which catalyzes a key step in the de novo synthesis of phosphatidylethanolamine (PE) in Leishmania major. Results showed that EPCT is indispensable for both promastigotes and amastigotes, indicating that de novo PE synthesis is still needed at certain capacity for the intracellular form of Leishmania parasites. In addition, elevated EPCT expression alters overall PE synthesis and compromises parasite’s tolerance to adverse conditions and is deleterious to the growth of intracellular amastigotes. These findings provide new insight into how Leishmania acquire essential phospholipids and how disturbance of lipid metabolism can impact parasite fitness.
Analysis of CDPK1 targets identifies a trafficking adaptor complex that regulates microneme exocytosis in Toxoplasma

Apicomplexan parasites use Ca2+-regulated exocytosis to secrete essential virulence factors from specialized organelles called micronemes. Ca2+-dependent protein kinases (CDPKs) are required for microneme exocytosis; however, the molecular events that regulate trafficking and fusion of micronemes with the plasma membrane remain unresolved. Here, we combine sub-minute resolution phosphoproteomics and bio-orthogonal labeling of kinase substrates in Toxoplasma gondii to identify 163 proteins phosphorylated in a CDPK1-dependent manner. In addition to known regulators of secretion, we identify uncharacterized targets with predicted functions across signaling, gene expression, trafficking, metabolism, and ion homeostasis. One of the CDPK1 targets is a putative HOOK activating adaptor. In other eukaryotes, HOOK homologs form the FHF complex with FTS and FHIP to activate dynein-mediated trafficking of endosomes along microtubules. We show the FHF complex is partially conserved in T. gondii, consisting of HOOK, an FTS homolog, and two parasite-specific proteins (TGGT1_306920 and TGGT1_316650). CDPK1 kinase activity and HOOK are required for the rapid apical trafficking of micronemes as parasites initiate motility. Moreover, parasites lacking HOOK or FTS display impaired microneme protein secretion, leading to a block in the invasion of host cells. Taken together, our work provides a comprehensive catalog of CDPK1 targets and reveals how vesicular trafficking has been tuned to support a parasitic lifestyle.