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检索条件"任意字段=Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming"

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A framework for practical parallel fast matrix multiplication 2015

A framework for practical parallel fast matrix multiplicatio...

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20th ACM SIGPLAN symposium on principles and practice of parallel programming, PPoPP 2015

作者： Benson, Austin R. Ballard, Grey Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Sandia National Laboratories LivermoreCA United States

ISBN: (纸本)9781450332057

Matrix multiplication is a fundamental computation in many scientific disciplines. In this paper, we show that novel fast matrix multiplication algorithms can significantly outperform vendor implementations of the classical algorithm and Strassen's fast algorithm on modest problem sizes and shapes. Furthermore, we show that the best choice of fast algorithm depends not only on the size of the matrices but also the shape. We develop a code generation tool to automatically implement multiple sequential and shared-memory parallel variants of each fast algorithm, including our novel parallelization scheme. this allows us to rapidly benchmark over 20 fast algorithms on several problem sizes. Furthermore, we discuss a number of practical implementation issues for these algorithms on shared-memory machines that can direct further research on making fast algorithms practical. Copyright 2015 ACM.

关键词： Matrix algebra

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Using Data Structure Knowledge for Efficient Lock Generation and Strong Atomicity

Using Data Structure Knowledge for Efficient Lock Generation...

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15th ACM SIGPLAN symposium on principles and practice of parallel programming

作者： Upadhyaya, Gautam Midkiff, Samuel P. Pai, Vijay S. Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781605587080

To achieve high-performance on multicore systems, shared-memory parallel languages must efficiently implement atomic operations. the commonly used and studied paradigms for atomicity are fine-grained locking, which is both difficult to program and error-prone;optimistic software transactions, which require substantial overhead to detect and recover from atomicity violations;and compiler-generation of locks from programmer-specified atomic sections, which leads to serialization whenever imprecise pointer analysis suggests the mere possibility of a conflicting operation. this paper presents a new strategy for compiler-generated locking that uses data structure knowledge to facilitate more precise alias and lock generation analyses and reduce unnecessary serialization. Implementing and evaluating these ideas in the Java language shows that the new strategy achieves eight-thread speedups of 0.83 to 5.9 for the five STAMP benchmarks studied, outperforming software transactions on all but one benchmark, and nearly matching programmer-specified fine-grained locks on all but one benchmark. the results also indicate that compiler knowledge of data structures improves the effectiveness of compiler analysis, boosting eight-thread performance by up to 300%. Further, the new analysis allows for software support of strong atomicity with less than 1% overhead for two benchmarks and less than 20% for three others. the strategy also nearly matches the performance of programmer-specified fine-grained locks for the SPECjbb2000 benchmark, which has traditionally not been amenable to static analyses.

关键词： Algorithms Performance Design Automatic lock generation transactional memory parallel programming

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proceedings of the 12th Annual ACM symposium on principles of Distributed Computing

Proceedings of the 12th Annual ACM Symposium on Principles o...

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proceedings of the 12th Annual ACM symposium on principles of Distributed Computing

ISBN: (纸本)0897916131

the symposium materials contain 24 papers. the topics covered include local area networks as distributed systems;operating systems research;scalable parallel computing;atomic snapshots;radio networks;fast deflection routing for packets and worms;parallel computing models;wait-free clock synchronization;optimal clock synchronization;partially synchronized clocks;resource bounds and combinations of consensus objects;scalable synchronization with minimal hardware support;adaptive solutions to the mutual exclusion problem;distributed fingerprints and secure information dispersal;replicated files;space complexity of randomized synchronization;lower bound on wait-free counting;and knowledge-oriented programming.

关键词： Distributed computer systems

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NUMA-aware Scheduling and Memory Allocation for data-flow task-parallel Applications 16

NUMA-aware Scheduling and Memory Allocation for data-flow ta...

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21st ACM SIGPLAN symposium on principles and practice of parallel programming (PPoPP)

作者： Drebes, Andi Pop, Antoniu Heydemann, Karine Drach, Nathalie Cohen, Albert Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England UPMC Paris 06 Sorbonne Univ CNRS LIP6UMR 7606 Paris France Inria Ecole Normale Super Rocquencourt France

ISBN: (纸本)9781450340922

Dynamic task parallelism is a popular programming model on shared-memory systems. Compared to data parallel loop-based concurrency, it promises enhanced scalability, load balancing and locality. these promises, however, are undermined by non-uniform memory access (NUMA) systems. We show that it is possible to preserve the uniform hardware abstraction of contemporary task-parallel programming models, for both computing and memory resources, while achieving near-optimal data locality. Our run-time algorithms for NUMA-aware task and data placement are fully automatic, application-independent, performance-portable across NUMA machines, and adapt to dynamic changes. Placement decisions use information about inter-task data dependences and reuse. this information is readily available in the run-time systems of modern task-parallel programming frameworks, and from the operating system regarding the placement of previously allocated memory. Our algorithms take advantage of data-flow style task parallelism, where the privatization of task data enhances scalability through the elimination of false dependences and enables fine-grained dynamic control over the placement of application data. We demonstrate that the benefits of dynamically managing data placement outweigh the privatization cost, even when comparing with target-specific optimizations through static, NUMA-aware data interleaving. Our implementation and the experimental evaluation on a set of high-performance benchmarks executing on a 192-core system with 24 NUMA nodes show that the fraction of local memory accesses can be increased to more than 9 9 %, resulting in a speedup of up to 5x compared to a NUMA-aware hierarchical work-stealing baseline.

关键词： Scalability

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Scaling implicit parallelism via dynamic control replication 21

Scaling implicit parallelism via dynamic control replication

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26th ACM SIGPLAN symposium on principles and practice of parallel programming, PPoPP 2021

作者： Bauer, Michael Lee, Wonchan Slaughter, Elliott Jia, Zhihao Di Renzo, Mario Papadakis, Manolis Shipman, Galen McCormick, Patrick Garland, Michael Aiken, Alex NVIDIA United States Slac National Accelerator Laboratory United States Carnegie Mellon University United States Sapienza University of Rome Italy Los Alamos National Laboratory United States Stanford University United States

ISBN: (纸本)9781450382946

We present dynamic control replication, a run-time program analysis that enables scalable execution of implicitly parallel programs on large machines through a distributed and efficient dynamic dependence analysis. Dynamic control replication distributes dependence analysis by executing multiple copies of an implicitly parallel program while ensuring that they still collectively behave as a single execution. By distributing and parallelizing the dependence analysis, dynamic control replication supports efficient, on-the-fly computation of dependences for programs with arbitrary control flow at scale. We describe an asymptotically scalable algorithm for implementing dynamic control replication that maintains the sequential semantics of implicitly parallel programs. An implementation of dynamic control replication in the Legion runtime delivers the same programmer productivity as writing in other implicitly parallel programming models, such as Dask or TensorFlow, while providing better performance (11.4X and 14.9X respectively in our experiments), and scalability to hundreds of nodes. We also show that dynamic control replication provides good absolute performance and scaling for HPC applications, competitive in many cases with explicitly parallel programming systems. © 2021 ACM.

关键词： parallel programming

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Scalable parallel minimum spanning forest computation 12

Scalable parallel minimum spanning forest computation

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17th ACM SIGPLAN symposium on principles and practice of parallel programming, PPoPP'12

作者： Nobari, Sadegh Cao, thanh-Tung Karras, Panagiotis Bressan, St´ephane National University of Singapore Singapore Rutgers University United States

ISBN: (纸本)9781450311601

the proliferation of data in graph form calls for the development of scalable graph algorithms that exploit parallel processing environments. One such problem is the computation of a graph's minimum spanning forest (MSF). Past research has proposed several parallel algorithms for this problem, yet none of them scales to large, high-density graphs. In this paper we propose a novel, scalable, parallel MSF algorithm for undirected weighted graphs. Our algorithm leverages Prim's algorithm in a parallel fashion, concurrently expanding several subsets of the computed MSF. Our effort focuses on minimizing the communication among different processors without constraining the local growth of a processor's computed subtree. In effect, we achieve a scalability that previous approaches lacked. We implement our algorithm in CUDA, running on a GPU and study its performance using real and synthetic, sparse as well as dense, structured and unstructured graph data. Our experimental study demonstrates that our algorithm outperforms the previous state-of-the-art GPU-based MSF algorithm, while being several order of magnitude faster than sequential CPU-based algorithms. Copyright © 2012 ACM.

关键词： Graphics processing unit

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An Adaptive Performance Modeling Tool for GPU Architectures

An Adaptive Performance Modeling Tool for GPU Architectures

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15th ACM SIGPLAN symposium on principles and practice of parallel programming

作者： Baghsorkhi, Sara S. Delahaye, Matthieu Patel, Sanjay J. Gropp, William D. Hwu, Wen-mei W. Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781605587080

this paper presents an analytical model to predict the performance of general-purpose applications on a GPU architecture. the model is designed to provide performance information to an auto-tuning compiler and assist it in narrowing down the search to the more promising implementations. It can also be incorporated into a tool to help programmers better assess the performance bottlenecks in their code. We analyze each GPU kernel and identify how the kernel exercises major GPU microarchitecture features. To identify the performance bottlenecks accurately, we introduce an abstract interpretation of a GPU kernel, work flow graph, based on which we estimate the execution time of a GPU kernel. We validated our performance model on the NVIDIA GPUs using CUDA (Compute Unified Device Architecture). For this purpose, we used data parallel benchmarks that stress different GPU microarchitecture events such as uncoalesced memory accesses, scratch-pad memory bank conflicts, and control flow divergence, which must be accurately modeled but represent challenges to the analytical performance models. the proposed model captures full system complexity and shows high accuracy in predicting the performance trends of different optimized kernel implementations. We also describe our approach to extracting the performance model automatically from a kernel code.

关键词： Design Measurement Performance Analytical model GPU parallel programming Performance estimation

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Efficient deterministic multithreading without global barriers 14

Efficient deterministic multithreading without global barrie...

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proceedings of the 19th ACM SIGPLAN symposium on principles and practice of parallel programming

作者： Lu, Kai Zhou, Xu Bergan, Tom Wang, Xiaoping Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China University of Washington Computer Science and Engineering United States

ISBN: (纸本)9781450326568

Multithreaded programs execute nondeterministically on conventional architectures and operating systems. this complicates many tasks, including debugging and testing. Deterministic multithreading (DMT) makes the output of a multithreaded program depend on its inputs only, which can totally solve the above problem. However, current DMT implementations suffer from a common inefficiency: they use frequent global barriers to enforce a deterministic ordering on memory accesses. In this paper, we eliminate that inefficiency using an execution model we call deterministic lazy release consistency (DLRC). Our execution model uses the Kendo algorithm to enforce a deterministic ordering on synchronization, and it uses a deterministic version of the lazy release consistency memory model to propagate memory updates across threads. Our approach guarantees that programs execute deterministically even when they contain data races. We implemented a DMT system based on these ideas (RFDet) and evaluated it using 17 parallel applications. Our implementation targets C/C++ programs that use POSIX threads. Results show that RFDet gains nearly 2x speedup compared with Dthreads-a start-of-the-art DMT system. Copyright © 2014 ACM.

关键词： C++ (programming language)

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Experiences using the parascope editor: An interactive parallel programming tool 93

Experiences using the parascope editor: An interactive paral...

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4th ACM SIGPLAN symposium on principles and practice of parallel programming, PPOPP 1993

作者： Hall, Mary W. Harvey, Timothy J. Kennedy, Ken McIntosh, Nathaniel McKinley, Kathryn S. Oldham, Jeffrey D. Paleczny, Michael H. Roth, Gerald Center for Integrated Systems Stanford University Palo AltoCA94305 United States Department of Computer Science Rice University HoustonTX77251-1892 United States 35 rue Saint Honoré Fontainebleau CedexF-77305 France

ISBN: (纸本)0897915895

the ParaScope Editor is an interactive parallel programming tool that assists knowledgeable users in developing scientific Fortran programs. It displays the results of sophisticated program analyses, provides a set of powerful interactive transformations, and supports program editing. this paper summarizes experiences of scientific programmers and tool designers using the ParaScope Editor. We evaluate existing features and describe enhancements in three key areas: user interface, analysis, and transformation. Many existing features prove crucial to successful program parallelization. they include interprocedural array side-effect analysis and program and dependence view filtering. Desirable functionality includes improved program navigation based on performance estimation, incorporating user assertions in analysis and more guidance in selecting transformations. these results offer insights for the authors of a variety of programming tools and parallelizing compilers. © 1993 ACM.

关键词： parallel programming

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Modernizing parallel code with pattern analysis 21

Modernizing parallel code with pattern analysis

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26th ACM SIGPLAN symposium on principles and practice of parallel programming, PPoPP 2021

作者： Lozano, Roberto Castañeda Cole, Murray Franke, Björn University of Edinburgh School of Informatics Edinburgh United Kingdom

ISBN: (纸本)9781450382946

Fifty years of parallel programming has generated a substantial legacy parallel codebase, creating a new portability challenge: re-parallelizing already parallel code. Our solution exploits inherently portable parallel patterns, and addresses the challenge of identifying patternization opportunities in legacy parallel code via constraint matching on traced dynamic dataflow graphs. Notably, this makes the analysis source-independent and equally applicable to sequential and parallel legacy code. We identify various map and reduction patterns, including compositions, in Pthreads code. Experiments with the Starbench suite show that our analysis is effective (finding 86% of the patterns known in the literature), accurate (reporting actual patterns in 98% of the cases), and efficient (scaling linearly with the size of the execution traces). We re-express the found patterns via a parallel pattern library, making code freely portable across CPU/GPU systems and performing competitively with hand-tuned implementations at zero additional effort. © 2021 ACM.

关键词： Pattern matching

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