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Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs 36

Optimizing Huffman Decoding for Error-Bounded Lossy Compress...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Rivera, Cody Di, Sheng Tian, Jiannan Yu, Xiaodong Tao, Dingwen Cappello, Franck Univ Alabama Dept Comp Sci Tuscaloosa AL 35487 USA Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL USA

ISBN: (纸本)9781665481069

More and more HPC applications require fast and effective compression techniques to handle large volumes of data in storage and transmission. Not only do these applications need to compress the data effectively during simulation, but they also need to perform decompression efficiently for post hoc analysis. SZ is an error-bounded lossy compressor for scientific data, and cuSZ is a version of SZ designed to take advantage of the GPU's power. At present, cuSZ's compression performance has been optimized significantly while its decompression still suffers considerably lower performance because of its sophisticated loss-less compression step-a customized Huffman decoding. In this work, we aim to significantly improve the Huffman decoding performance for cuSZ, thus improving the overall decompression performance in turn. To this end, we first investigate two state-of-the-art GPU Huffman decoders in depth. Then, we propose a deep architectural optimization for both algorithms. Specifically, we take full advantage of CUDA GPU architectures by using shared memory on decoding/writing phases, online tuning the amount of shared memory to use, improving memory access patterns, and reducing warp divergence. Finally, we evaluate our optimized decoders on an Nvidia V100 GPU using eight representative scientific datasets. Our new decoding solution obtains an average speedup of 3.64x over cuSZ's Huffman decoder and improves its overall decompression performance by 2.43x on average.

关键词： distributed processing Analytical models Memory management Graphics processing units distributed databases Throughput Data models

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Degree-Aware Kernels for computing Jaccard Weights on GPUs 36

Degree-Aware Kernels for Computing Jaccard Weights on GPUs

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Aljundi, Amro Alabsi Akyildiz, Taha Atahan Kaya, Kamer Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey

ISBN: (纸本)9781665481069

Graphs provide the ability to extract valuable metrics from the structural properties of the underlying data they represent. One such metric is the Jaccard Weight of an edge, which is the ratio of the number of common neighbors of the edge's endpoints to the union of the endpoints' neighborhood. A naive implementation of Jaccard Weights computation has a complexity that scales with the number of edges in the graph times the square of the maximum degree. Recently, GPU-based parallel algorithms have been proposed for this problem. However, these algorithms cannot overcome the structural variance within a graph, i.e., the sparsity pattern and degree imbalance, which directly translates to unbalanced work distribution across threads. In this work, we propose an optimized GPU-based algorithm with an ML-based work distribution model that mitigates the unbalanced work distribution. Our algorithm is shown to be up to 35x and on average 12x faster than the state of the art in practice while using less memory. In fact, we show that by manually tweaking the load distribution, a state-of-the-art implementation can be 5x faster. In addition, we propose a multi-core, shared-memory algorithm that applies a traditional but effective technique to improve the computation asymptotically and perform comparably to the GPU algorithms. Our code is available at https://***/SU- HPC/Jaccard-ML

关键词： Jaccard Weights parallel graph algorithms GPU work distribution

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Empirical Study of Molecular Dynamics Workflow Data Movement: DYAD vs. Traditional I/O Systems

Empirical Study of Molecular Dynamics Workflow Data Movement...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Lumsden, Ian Devarajan, Hariharan Marquez, Jack Brink, Stephanie Boehme, David Pearce, Olga Yeom, Jae-Seung Taufer, Michela Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Lawrence Livermore Natl Lab Livermore CA 94550 USA

ISBN: (纸本)9798350364613;9798350364606

This experimental work examines data movement in molecular dynamics (MD) workflows, comparing the Dynamic and Asynchronous Data Streamliner (DYAD) middleware with traditional, industry-standard I/O systems such as XFS and Lustre. DYAD moves MD simulation frames to analytics processes, providing enhanced flexibility and efficiency for dynamic data transfers and in situ analytics. At the same time, traditional I/O storage systems provide durability and scalability for high-performance computing (HPC) systems. The study integrates MD workflows with common simulation codes, facilitating immediate capture and transfer of MD frames to a staging area. It explores various molecular models, from simple to complex, assessing data management performance and scalability. Different producer-consumer pairs, molecular models, and data transaction frequency enable testing across small to large-scale HPC scenarios, from single-node configurations to large, distributed environments. The findings reveal that adaptive mechanisms for minimizing synchronization, direct network communication between producer and consumer processes, and optimizations of both data movement and synchronization are crucial for performance and scalability in MD workflows.

关键词： Producer-Consumer Paradigm Molecular Dynamics Workflows Dynamic Data Management High-Performance computing In Situ Analytics

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Combining Lossy Compression with Multi-level Caching for Data Staging over Network

Combining Lossy Compression with Multi-level Caching for Dat...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Aoyagi, Rei Takahashi, Keichi Shimomura, Yoichi Takizawa, Hiroyuki Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan Tohoku Univ Cybersci Ctr Sendai Miyagi Japan

ISBN: (纸本)9798350364613;9798350364606

Researchers conduct post-processing on the simulation results by running an interactive data analysis tool on a High-Performance computing (HPC) system installed at an HPC center and retrieving the post-processed results. Certain data analysis scenarios require to transfer the simulation results directly from the center. in such scenarios, a portion of the data would usually be streamed over the network to achieve interactivity. However, there still exist two challenges in maintaining interactivity: (1) limited network bandwidth and (2) long network latency. To tackle these challenges, we propose a system to enable interactive array analysis over the network. We employ error-bounded lossy compression to increase the effective network bandwidth. Furthermore, we employ multi-level caching to hide the network latency and combine prefetching to improve the cache hit ratio. The cache replacement and prefetching policies are designed considering the data access pattern of interactive analysis. We compared our proposed system with TileDB, one of the state-of-the-art array databases, by measuring the average latency for various access patterns. Compared to TileDB, the proposed system reduces the average latency by up to 91.6% by allowing 10% of error because the cache hit ratio was improved by more than 40% due to the proper cache replacement and prefetching policy and network transfer time was reduced more than 75% by using lossy compression.

关键词： HPC distributed computing Data Staging Lossy Compression Interactive Analysis Bandwidth Software Caching

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A Streaming System for Large-scale Temporal Graph Mining of Reddit Data 36

A Streaming System for Large-scale Temporal Graph Mining of ...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Huber, Andreas Schroeder, Daniel Thilo Pogorelov, Konstantin Griwodz, Carsten Langguth, Johannes Univ Oslo Simula Res Lab Oslo Norway Simula Res Lab Oslo Norway Univ Oslo Oslo Norway BI Norwegian Business Sch Simula Res Lab Oslo Norway

ISBN: (纸本)9781665497473

The study of online social networks has become a major topic of research within the last decade, and many aspects of the networks and the behavior of their users have been investigated. The majority of research efforts has been directed at Twitter, which grants limited data accesses to researchers and provides detailed information. However, recently, important social and economic phenomena such as WallStreetBets or Antiwork have originated on Reddit, which has thus become an important field of investigation in its own right, and, due to its open nature, all Reddit data is available to study. As a consequence, in contrast to Twitter, where it is difficult to obtain large amounts of data, the main challenge of researching Reddit is to handle the vast amounts of data that are freely available. Here, we present the Reddit Dataset Stream Pipeline (RDSP), a simple and efficient parallel system based on Akka Streams that is capable of processing the entire Reddit dataset. We demonstrate how to build massive temporal graphs between subreddits from a parallel streamed dataset. We investigate the generated graphs and present experimental results. Moreover, we publish both the datasets as well as the codebase in order to invite researchers from different fields to contribute and profit from this work.

关键词： Reddit Stream processing parallel Processing Computational Social Science

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PE-FedAvg: A Privacy-Enhanced Federated Learning for distributed Android Malware Detection 21

PE-FedAvg: A Privacy-Enhanced Federated Learning for Distrib...

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21st IEEE international symposium on parallel and distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud computing, 16th IEEE international Conference on Social computing and Networking and 13th international Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Tang, Junwei Xu, Zilong Ye, Luyao Peng, Tao He, Ruhan Hu, Xinrong Tian, Wenlong Wuhan Textile University Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion China Wuhan Textile University School of Computer Science and Artificial Intelligence China University of South China School of Computer Science China Nanyang Technological University School of Physical and Mathematical Sciences Singapore

ISBN: (纸本)9798350329223

Android malware detection has become research hotspot in mobile security. When security service providers obtain feature information from target samples, they may involve user privacy information such as identity and behavioral habits, creating an urgent need for a distributed machine learning model to carry out malicious application detection with privacy protection. However, the information exchange between clients and servers in basic federated learning still poses risks of model data leakage, and the likelihood of inferring privacy information from the original feature data is high. Therefore, This paper propose a privacy-enhanced federated learning system called PE-FedAvg for distributed Android malware detection. Firstly, Gaussian noise is added to the feature data uploaded from the client to the server, satisfying differential privacy characteristics while retaining data distribution for privacy protection. Secondly, an improved homomorphic encryption algorithm based on modular repeated squaring is used to encrypt and transmit the gradient weight information exchanged between federated learning clients and servers, preventing model-targeted attacks. By conducting the experiments on the Drebin and Malgenome datasets, our design has demonstrated exceptional performance compared to the state-of-the-art work, achieving accuracy rates of 98.61% and 99.3%, respectively. This achievement highlights that our approach not only enhances the security of the federated learning system, but also more effectively accomplishes the detection of Android malware. © 2023 IEEE.

关键词： Android malware

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Trusted Auditing of Data Operation Behaviors in Cloud based on Blockchain and TEE 21

Trusted Auditing of Data Operation Behaviors in Cloud based ...

引用

作者： Xu, Shujiang Wang, Fansheng Wang, Lianhai Mihaljevic, Miodrag J. Zhang, Shuhui Shao, Wei Huang, Qihao Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Mathematical Institute The Serbian Academy of Sciences and Arts Belgrade Serbia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350329223

With the rapid development of the new generation of information technologies, including cloud computing, 5G, and the Internet of Things, data outsourcing storage in the cloud has brought great convenience to data storage or access anytime and anywhere. Due to the separation of ownership and management of outsourced stored data, it faces data security risks, including data loss and personal privacy disclosure. The third-party audit of data operational behavior by logs is often employed to strengthen the security of outsourced data. However, some privacy breach incidents in recent years show that third parties who may leak information about data and users' identities are not always trustworthy. To achieve data security and privacy protection, an operation behavior auditing scheme for outsourced data is proposed with the blockchain rather than third parties. The data operation behavior audit is carried out from three dimensions: integrity of the operation behavior log, the credibility of the operation behavior information, and compliance of operation behavior. The security analysis and experiment results indicate that this scheme is secure and feasible. © 2023 IEEE.

关键词： Blockchain

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P-ckpt: Coordinated Prioritized Checkpointing 36

P-ckpt: Coordinated Prioritized Checkpointing

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Behera, Subhendu Wan, Lipeng Mueller, Frank Wolf, Matthew Klasky, Scott North Carolina State Univ Raleigh NC 27695 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781665481069

Good prediction accuracy and adequate lead time to failure are key to the success of failure-aware Checkpoint/Restart (C/R) models on current and future large-scale High-Performance computing (HPC) systems. This paper develops a novel checkpointing technique, called p-ckpt, that aims to maintain the performance efficiency of failure-aware C/R models even when failures are predicted with a small lead time. The p-ckpt technique is developed for HPC systems with multi-level memory systems to prioritize checkpoints from vulnerable nodes (nodes with predicted failure) in the event of failure prediction. It applies coordination among the nodes within an application so that vulnerable nodes' checkpoint data is stored to the parallel File System (PFS) first by assigning priorities based on the lead time to failure. Vulnerable nodes thus have low-latency access on the critical path to the PFS before any failure happens. Further, we create the hybrid p-ckpt model by integrating Live Migration (LM) because of its cost-effectiveness and to reduce checkpoint frequency. Our hybrid p-ckpt C/R model considers prediction lead time and checkpoint latency to the PFS to decide on a feasible proactive action such as p-ckpt and LM. Simulations of six real-world applications for the Summit supercomputer show a approximate to 53-65% reduction in overhead due to the hybrid p-ckpt model compared to a approximate to 31-61% reduction in a state-of-the-art solution. We assess our C/R models against multiple failure distributions and consider lead time variability and failure prediction accuracy. Based on this evaluation and assessment, we discuss the trade-offs of using these models and their impact on application overhead.

关键词： Fault Tolerance High-Performance computing Failure Prediction I/O subsystem Checkpoint/Restart Live Migration Burst Buffers

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Aspect-Oriented Programming based building block platform to construct Domain-Specific Language for HPC application 36

Aspect-Oriented Programming based building block platform to...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Ishimura, Osamu Yoshimoto, Yoshihide Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan

ISBN: (纸本)9781665497473

The world of HPC systems is changing to a more complicated system because the performance improvement of processors has been slowed down. One of the promising approaches is Domain-Specific Language (DSL), which provides a productive environment to create a high-efficient program without pain. However, existing DSL platforms themselves often lack portability and cost DSL developers great effort. To solve this issue, we propose an Aspect-Oriented Programming (AOP) based DSL constructing platform, enabling developers to build a DSL platform by combining Aspect modules. Aspect modules manage abstracted application flow, data structure, and memory access on our platform. Therefore, developers can create any DSL platform whose target application has the attributes which HPC applications usually have, the abstraction assumes. This study implemented a prototype platform that can handle MPI and OpenMP layers. The prototype supports three types of applications (Structured-Grid, Unstructured-Grid, and Particle Simulation). Then, we evaluated the overheads caused by achieving flexibility and productivity of the platform.

关键词： Domain-Specific Language Aspect-Oriented Programming High-Performance computing

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Locality-aware Load-Balancing For Serverless Clusters 22

Locality-aware Load-Balancing For Serverless Clusters

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31st international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Fuerst, Alexander Sharma, Prateek Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9781450391993

While serverless computing provides more convenient abstractions for developing and deploying applications, the Function-as-a-Service (FaaS) programming model presents new resource management challenges for the FaaS provider. In this paper, we investigate load-balancing policies for serverless clusters. Locality, i.e., running repeated invocations of a function on the same server, is a key determinant of performance because it increases warm-starts and reduces cold-start overheads. We find that the locality vs. load tradeoff is crucial and presents a large design space. We enhance consistent hashing for FaaS, and develop CH-RLU: Consistent Hashing with Random Load Updates, a simple practical load-balancing policy which provides more than 2x reduction in function latency. Our policy deals with highly heterogeneous, skewed, and bursty function workloads, and is a drop-in replacement for OpenWhisk's existing load-balancer. We leverage techniques from caching such as SHARDS for popularity detection, and develop a new approach that places functions based on a tradeoff between locality, load, and randomness.

关键词： Functions as a Service Serverless computing Cloud computing Load Balancing

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