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SYNC or ASYNC: Time to fuse for distributed graph-parallel computation 2015

SYNC or ASYNC: Time to fuse for distributed graph-parallel c...

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20th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2015

作者： Xie, Chenning Chen, Rong Guan, Haibing Zang, Binyu Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science Shanghai Jiao Tong University China

ISBN: (纸本)9781450332057

Large-scale graph-structured computation usually exhibits iterative and convergence-oriented computing nature, where input data is computed iteratively until a convergence condition is reached. Such features have led to the development of two different computation modes for graph-structured programs, namely synchronous (Sync) and asynchronous (Async) modes. Unfortunately, there is currently no in-depth study on their execution properties and thus programmers have to manually choose a mode, either requiring a deep understanding of underlying graph engines, or suffering from suboptimal performance. this paper makes the first comprehensive characterization on the performance of the two modes on a set of typical graph-parallel applications. Our study shows that the performance of the two modes varies significantly with different graph algorithms, partitioning methods, execution stages, input graphs and cluster scales, and no single mode consistently outperforms the other. To this end, this paper proposes Hsync, a hybrid graph computation mode that adaptively switches a graph-parallel program between the two modes for optimal performance. Hsync constantly collects execution statistics on-the-fly and leverages a set of heuristics to predict future performance and determine when a mode switch could be profitable. We have built online sampling and offline profiling approaches combined with a set of heuristics to accurately predicting future performance in the two modes. A prototype called PowerSwitch has been built based on PowerGraph, a state-of-the-art distributed graph-parallel system, to support adaptive execution of graph algorithms. On a 48-node EC2-like cluster, PowerSwitch consistently outperforms the best of both modes, with a speedup ranging from 9% to 73% due to timely switch between two modes. Copyright 2015 acm.

关键词： Graphic methods

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Reference idempotency analysis: A framework for optimizing speculative execution

Reference idempotency analysis: A framework for optimizing s...

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8th acm sigplan symposium on principles and practice of parallel programming

作者： Seon Wook Kim Ooi, C.-L. Eigenmann, R. Falsafi, B. Vijaykumar, T.N. Intel Corp. Champaign IL United States

Recent proposals for multithreaded architectures allow threads with unknown dependences to execute speculatively in parallel. these architectures use hardware speculative storage to buffer uncertain data, track data dependences and roll back incorrect executions. Because all memory references access the speculative storage, current proposals implement this storage using small memory structures for fast access. the limited capacity of the speculative storage causes considerable performance loss due to speculative storage overflow whenever a thread's speculative state exceeds the storage capacity. Larger threads exacerbate the over-flow problem but are preferable to smaller threads, as larger threads uncover more parallelism. In this paper, we discover a new program property called memory reference idempotency. Idempotent references need not be tracked in the speculative storage, and instead can directly access non-speculative storage (i.e., the conventional memory hierarchy). thus, we reduce the demand fo r speculative storage space. We define a formal framework for reference idempotency and present a novel compiler-assisted speculative execution model. We prove the necessary and sufficient conditions for reference idempotency using our model. We present a compiler algorithm to label idempotent memory references for the hardware. Experimental results show that for our benchmarks, over 60% of the references in non-parallelizable program sections are idempotent.

关键词： parallel processing systems

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A programming Model for GPU Load Balancing 23

A Programming Model for GPU Load Balancing

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28th acm sigplan Annual symposium on principles and practice of parallel programming, PPoPP 2023

作者： Osama, Muhammad Porumbescu, Serban D. Owens, John D. University of California DavisCA United States

ISBN: (纸本)9798400700156

We propose a GPU fine-grained load-balancing abstraction that decouples load balancing from work processing and aims to support both static and dynamic schedules with a programmable interface to implement new load-balancing schedules. Prior to our work, the only way to unleash the GPU's potential on irregular problems has been to workload-balance through application-specific, tightly coupled load-balancing techniques. With our open-source framework for load-balancing, we hope to improve programmers' productivity when developing irregular-parallel algorithms on the GPU, and also improve the overall performance characteristics for such applications by allowing a quick path to experimentation with a variety of existing load-balancing techniques. Consequently, we also hope that by separating the concerns of load-balancing from work processing within our abstraction, managing and extending existing code to future architectures becomes easier. © 2023 Owner/Author.

关键词： Graphics processing unit

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Prolog programming with a map-reduce parallel construct 13

Prolog programming with a map-reduce parallel construct

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15th symposium on principles and practice of Declarative programming, PPDP 2013

作者： Côrte-Real, Joana Dutra, Inês Rocha, Ricardo CRACS INESC TEC University of Porto Rua Campo Alegre 1021/1055 4169-007 Porto Portugal

ISBN: (纸本)9781450321549

Map-Reduce is a programming model that has its roots in early functional programming. In addition to producing short and elegant code for problems involving lists or collections, this model has proven very useful for large-scale highly parallel data processing. In this work, we present the design and implementation of a high-level parallel construct that makes the Map-Reduce programming model available for Prolog programmers. To the best of our knowledge, there is no Map-Reduce framework native to Prolog, and so the aim of this work is to offer data processing features from which several applications can greatly benefit;the Inductive Logic programming field, for instance, can take advantage of a Map-Reduce predicate when proving newly created rules against sets of examples. Our Map-Reduce model was comprehensively tested with different applications. Our experiments, using the Yap Prolog system, show that: (i) the model scales linearly up to 24 processors;(ii) a dynamic distributed scheduling strategy performs better than centralized or static scheduling strategies;and (iii) the performance varies significantly with the number of items being sent to each processor at a time. Overall, our Map-Reduce framework presents as a good alternative for both taking advantage of the currently available low cost multi-core architectures and developing scalable data processing applications, native to the Prolog programming language. © 2013 acm.

关键词： Computer circuits

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Exploring the Use of WebAssembly in HPC 23

Exploring the Use of WebAssembly in HPC

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28th acm sigplan Annual symposium on principles and practice of parallel programming, PPoPP 2023

作者： Chadha, Mohak Krueger, Nils John, Jophin Jindal, Anshul Gerndt, Michael Benedict, Shajulin Computer Architecture and Parallel Systems Technische Universität München Germany Department of Computer Science and Engg. Indian and Institute of Information Technology Kottayam Kerala India

ISBN: (纸本)9798400700156

Containerization approaches based on namespaces offered by the Linux kernel have seen an increasing popularity in the HPC community both as a means to isolate applications and as a format to package and distribute them. However, their adoption and usage in HPC systems faces several challenges. these include difficulties in unprivileged running and building of scientific application container images directly on HPC resources, increasing heterogeneity of HPC architectures, and access to specialized networking libraries available only on HPC systems. these challenges of container-based HPC application development closely align with the several advantages that a new universal intermediate binary format called WebAssembly (Wasm) has to offer. these include a lightweight userspace isolation mechanism and portability across operating systems and processor architectures. In this paper, we explore the usage of Wasm as a distribution format for MPI-based HPC applications. To this end, we present MPIWasm, a novel Wasm embedder for MPI-based HPC applications that enables high-performance execution of Wasm code, has low-overhead for MPI calls, and supports high-performance networking interconnects present on HPC systems. We evaluate the performance and overhead of MPIWasm on a production HPC system and AWS Graviton2 nodes using standardized HPC benchmarks. Results from our experiments demonstrate that MPIWasm delivers competitive native application performance across all scenarios. Moreover, we observe that Wasm binaries are 139.5x smaller on average as compared to the statically-linked binaries for the different standardized benchmarks. © 2023 acm.

关键词： Containers

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A Peta-scalable CPU-GPU Algorithm for Global Atmospheric Simulations 13

A Peta-scalable CPU-GPU Algorithm for Global Atmospheric Sim...

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18th acm sigplan symposium on principles and practice of parallel programming

作者： Yang, Chao Xue, Wei Fu, Haohuan Gan, Lin Li, Linfeng Xu, Yangtong Lu, Yutong Sun, Jiachang Yang, Guangwen Zheng, Weimin Chinese Acad Sci Inst Software Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Minist Educ Key Lab Earth Syst Modeling Beijing 100084 Peoples R China Tsinghua Univ Ctr Earth Syst Sci Beijing 100084 Peoples R China Natl Univ Def Technol Dept Comp Sci & Technol Changsha Hunan Peoples R China Chinese Acad Sci State Key Lab Space Weather Beijing Peoples R China

ISBN: (纸本)9781450319225

Developing highly scalable algorithms for global atmospheric modeling is becoming increasingly important as scientists inquire to understand behaviors of the global atmosphere at extreme scales. Nowadays, heterogeneous architecture based on both processors and accelerators is becoming an important solution for large-scale computing. However, large-scale simulation of the global atmosphere brings a severe challenge to the development of highly scalable algorithms that fit well into state-of-the-art heterogeneous systems. Although successes have been made on GPU-accelerated computing in some top-level applications, studies on fully exploiting heterogeneous architectures in global atmospheric modeling are still very less to be seen, due in large part to both the computational difficulties of the mathematical models and the requirement of high accuracy for long term simulations. In this paper, we propose a peta-scalable hybrid algorithm that is successfully applied in a cubed-sphere shallow-water model for global atmospheric simulations. We employ an adjustable partition between CPUs and GPUs to achieve a balanced utilization of the entire hybrid system, and present a pipe-flow scheme to conduct conflict-free inter-node communication on the cubed-sphere geometry and to maximize communication-computation overlap. Systematic optimizations for multithreading on both GPU and CPU sides are performed to enhance computing throughput and improve memory efficiency. Our experiments demonstrate nearly ideal strong and weak scalabilities on up to 3,750 nodes of the Tianhe-1A. the largest run sustains a performance of 0.8 Pflops in double precision (32% of the peak performance), using 45,000 CPU cores and 3,750 GPUs.

关键词： parallel algorithm atmospheric modeling GPU heterogeneous system communication-computation overlap scalability

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A hierarchical approach to reducing communication in parallel graph algorithms 2015

A hierarchical approach to reducing communication in paralle...

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20th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2015

作者： Harshvardhan Amato, Nancy M. Rauchwerger, Lawrence Parasol Laboratory Department of Computer Science and Engineering Texas A and M University United States

ISBN: (纸本)9781450332057

Large-scale graph computing has become critical due to the ever-increasing size of data. However, distributed graph computations are limited in their scalability and performance due to the heavy communication inherent in such computations. this is exacerbated in scale-free networks, such as social and web graphs, which contain hub vertices that have large degrees and therefore send a large number of messages over the network. Furthermore, many graph algorithms and computations send the same data to each of the neighbors of a vertex. Our proposed approach recognizes this, and reduces communication performed by the algorithm without change to user-code, through a hierarchical machine model imposed upon the input graph. the hierarchical model takes advantage of locale information of the neighboring vertices to reduce communication, both in message volume and total number of bytes sent. It is also able to better exploit the machine hierarchy to further reduce the communication costs, by aggregating traffic between different levels of the machine hierarchy. Results of an implementation in the STAPL GL shows improved scalability and performance over the traditional level-synchronous approach, with 2.5 × - 8× improvement for a variety of graph algorithms at 12, 000+ cores.

关键词： Big data

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Optimizing Data Popularity Conscious Bloom Filters

Optimizing Data Popularity Conscious Bloom Filters

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27th Annual acm symposium on principles of Distributed Computing

作者： Zhong, Ming Lu, Pin Shen, Kai Seiferas, Joel Google Inc. Department of Computer Science University of Rochester University of Rochester

ISBN: (纸本)9781595939890

Bloom filters are compact set, representations that support set membership queries with small, one-sided error probabilities. Standard Bloom filters are oblivious to object popularity ill sets, and membership queries. However, sets and queries in many distributed applications follow known, stable, highly skewed distributions (e.g., Zipf-like). this paper studies the problem of minimizing the, false-positive probability of a Bloom filter by adapting the number of hashes used for each data object to its popularity in sets and membership queries. We model the problem as a constrained nonlinear integer program and propose two polynomial-time solutions with bounded approximation ratios - one is a 2-approximation algorithm with O(N-c) running time (c >= 6 in practice): the other is a (2 + epsilon)-approximation algorithm with running time O(N-2/epsilon), epsilon > 0. Here N denotes the total number of distinct data objects that appear in set's or queries. We quantitatively evaluate our proposed approach oil two distributed applications (cooperative caching and full-text keyword searching) driven by real-life data traces. Compared to standard Bloom filters, our data popularity-conscious Bloom filters achieve up to 24 and 27 times false-positive probability reduction for the two applications respectively. the quantitative evaluation also validates our solution's bounded approximation ratio to the optimal.

关键词： Bloom filters optimization dynamic programming knapsack problems

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Training one DeePMD Model in Minutes: a Step towards Online Learning 24

Training one DeePMD Model in Minutes: a Step towards Online ...

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29th acm sigplan Annual symposium on principles and practice of parallel programming (PPoPP)

作者： Hu, Siyu Zhao, Tong Sha, Qiuchen Li, Enji Meng, Xiangyu Liu, Liping Wang, Lin-Wang Tan, Guangming Jia, Weile Chinese Acad Sci Inst Comp Technol State Key Lab Proc Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China China Univ Petr Qingdao Inst Software Coll Comp Sci & Technol Qingdao Peoples R China Chinese Acad Sci Inst Semicond Beijing Peoples R China

ISBN: (纸本)9798400704352

Neural Network Molecular Dynamics (NNMD) has become a major approach in material simulations, which can speedup the molecular dynamics (MD) simulation for thousands of times, while maintaining ab initio accuracy, thus has a potential to fundamentally change the paradigm of material simulations. However, there are two time-consuming bottlenecks of the NNMD developments. One is the data access of ab initio calculation results. the other, which is the focus of the current work, is reducing the training time of NNMD model. the training of NNMD model is different from most other neural network training because the atomic force (which is related to the gradient of the network) is an important physical property to be fit. Tests show the traditional stochastic gradient methods, like the Adam algorithms, cannot efficiently deploy the multisample minibatch algorithm. As a result, a typical training (taking the Deep Potential Molecular Dynamics (DeePMD) as an example) can take many hours. In this work, we designed a heuristic minibatch quasi-Newtonian optimizer based on Extended Kalman Filter method. An early reduction of gradient and error is adopted to reduce memory footprint and communication. the memory footprint, communication and settings of hyper-parameters of this new method are analyzed in detail. Computational innovations such as customized kernels of the symmetry-preserving descriptor are applied to exploit the computing power of the heterogeneous architecture. Experiments are performed on 8 different datasets representing different real case situations, and numerical results show that our new method has an average speedup of 32.2 compared to the Reorganized Layer-wised Extended Kalman Filter with 1 GPU, reducing the absolute training time of one DeePMD model from hours to several minutes, making it one step toward online training.

关键词： parallel training Molecular dynamics First principle ab initio GPU

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parallel binary code analysis 21

Parallel binary code analysis

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26th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2021

作者： Meng, Xiaozhu Anderson, Jonathon M. Mellor-Crummey, John Krentel, Mark W. Miller, Barton P. Milaković, Sran Department of Computer Science Rice University Houston TX United States Computer Sciences Department University of Wisconsin-Madison MadisonWI United States

ISBN: (纸本)9781450382946

Binary code analysis is widely used to help assess a program's correctness, performance, and provenance. Binary analysis applications often construct control flow graphs, analyze data flow, and use debugging information to understand how machine code relates to source lines, inlined functions, and data types. To date, binary analysis has been single-threaded, which is too slow for convenient use in performance tuning workflows where it is used to help attribute performance to complex applications with large binaries. this paper describes our design and implementation for accelerating the task of constructing control flow graphs (CFGs) from binaries by using multithreading. Prior research focuses on algorithms for analysis of challenging code constructs encountered while constructing CFGs, including functions sharing code, jump tables, non-returning functions, and tail calls. these algorithms are described from a program analysis perspective and are not suitable for direct parallel implementation. We abstract the task of constructing CFGs as repeated applications of several core CFG operations that include creating functions, basic blocks, and edges. We then derive CFG operation dependency, commutativity, and monotonicity. these operation properties guide our design of a new parallel analysis for constructing CFGs. Using 64 threads, we achieved as much as 25× speedup for constructing CFGs and 8× for a performance analysis tool that leverages our new analysis to recover program structure. © 2021 acm.

关键词： Graphic methods

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