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25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Xia, Yang Jiang, Peng Agrawal, Gagan Ohio State Univ Columbus OH 43210 USA Univ Iowa Iowa City IA USA

ISBN: (纸本)9781450368186

A finite-state machine (FSM) is a key component for many important applications, such as Huffman decoding, regular expression matching and HTML tokenization. Due to its inherent dependencies and unpredictable memory access pattern, FSM computations are considered to be extremely difficult to parallelize. As such, significant research efforts have been made to accelerate FSM computations. Although they achieve promising performance results on multi-core machines, these methods are not scalable for emerging many-core architectures such as the GPUs. Based on our experiments, we point out that the bottleneck of achieving scalability on GPUs is the sequential merge inherent to these methods. However, unlike the case for simple reduction loops, parallel merge implementations for FSM computations typically require runtime checks and re-executions, which can also impede performance. Based on these observations, we develop parallel merge techniques that select efficient runtime check implementations and avoids unnecessary re-executions. Further, based on GPU architectural features, we develop optimization techniques to improve performance. We evaluate our parallel merge implementations on a set of representative algorithms. Experimental results show that our parallel merge implementations are 2.02-6.74 times more efficient than corresponding sequential merge implementations and achieve better scalability on an Nvidia V100 GPU.

关键词： Finite-State Machines Speculation GPUs

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RaftLib: A C plus plus template library for high performance stream parallel processing

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2017年第5期31卷 391-404页

作者： Beard, Jonathan C. Li, Peng Chamberlain, Roger D. ARM Res 5707 Southwest Pkwy Suite 100 Austin Austin TX 78735 USA Amazon Inc Seattle WA USA Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA

Stream processing is a compute paradigm that has been around for decades, yet until recently has failed to garner the same attention as other mainstream languages and libraries (e.g. C++, OpenMP, MPI). Stream processing has great promise: the ability to safely exploit extreme levels of parallelism to process huge volumes of streaming data. there have been many implementations, both libraries and full languages. the full languages implicitly assume that the streaming paradigm cannot be fully exploited in legacy languages, while library approaches are often preferred for being integrable with the vast expanse of extant legacy code. Libraries, however are often criticized for yielding to the shape of their respective languages. RaftLib aims to fully exploit the stream processing paradigm, enabling a full spectrum of streaming graph optimizations, while providing a platform for the exploration of integrability with legacy C/C++ code. RaftLib is built as a C++ template library, enabling programmers to utilize the robust C++ standard library, and other legacy code, along with RaftLib's parallelization framework. RaftLib supports several online optimization techniques: dynamic queue optimization, automatic parallelization, and real-time low overhead performance monitoring.

关键词： Stream processing big-data C plus plus template library high performance computing RaftLib performance monitoring parallel processing

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No Barrier in the Road: A Comprehensive Study and Optimization of ARM Barriers 20

No Barrier in the Road: A Comprehensive Study and Optimizati...

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25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Liu, Nian Zang, Binyu Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Scalable Comp & Syst Shanghai Peoples R China

ISBN: (纸本)9781450368186

In this paper, we present the first comprehensive performance characterization and optimization of ARM barriers on both mobile and server platforms. We draw a set of observations through several abstracted models and validate them in scenarios where barriers are intensively used. We find that (1) order-preserving approaches without involving the bus significantly outperform other approaches, and (2) the tremendous overhead mostly comes from barriers strictly following remote memory references. Usually, such barriers are inserted when threads are exchanging data, and they are used to ensure the relative order between storing the data to a shared buffer and setting a flag to inform the receiver. Based on the observations, we propose a new mechanism, Pilot, to remove such barriers by leveraging the single-copy atomicity to piggyback the flag with the data. Applying Pilot only requires minor changes to applications and provides 10%-360% performance improvements in multiple benchmarks, which are close to the ideal performance without barriers.

关键词： barrier synchronization concurrency lock

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parallel skeletons for structured composition

Parallel skeletons for structured composition

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Proceedings of the 5th acm sigplan symposium on principles and practice of parallel programming

作者： Darlington, John Guo, Yi-ke To, Hing Wing Yang, Jin Imperial Coll London United Kingdom

In this paper, we propose a straightforward solution to the problems of compositional parallel programming by using skeletons as the uniform mechanism for structured composition. In our approach parallel programs are constructed by composing procedures in a conventional base language using a set of high-level, predefined, functional, parallel computational forms known as skeletons. the ability to compose skeletons provides us with the essential tools for building further and more complex application-oriented skeletons specifying important aspects of parallel computation. Compared with the process network based composition approach, such as PCN, the skeleton approach abstracts away the fine details of connecting communication ports to the higher level mechanism of making data distributions conform, thus avoiding the complexity of using lower level ports as the means of interaction. thus, the framework provides a natural integration of the compositional programming approach with the data parallel programming paradigm.

关键词： parallel processing systems

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Using GPU's to Accelerate Stencil-based Computation Kernels for the Development of Large Scale Scientific Applications on Heterogeneous Systems 12

Using GPU's to Accelerate Stencil-based Computation Kernels ...

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17th acm sigplan symposium on principles and practice of parallel programming

作者： Tao, Jian Blazewicz, Marek Brandt, Steven R. Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Poznan Supercomp & Networking Ctr Applicat Dept Poznan Poland

ISBN: (纸本)9781450311601

We present CaCUDA - a GPGPU kernel abstraction and a parallel programming framework for developing highly efficient large scale scientific applications using stencil computations on hybrid CPU/GPU architectures. CaCUDA is built upon the Cactus computational toolkit, an open source problem solving environment designed for scientists and engineers. Due to the flexibility and extensibility of the Cactus toolkit, the addition of a GPGPU programming framework required no changes to the Cactus infrastructure, guaranteeing that existing features and modules will continue to work without modification. CaCUDA was tested and benchmarked using a 3D CFD code based on a finite difference discretization of Navier-Stokes equations.

关键词： Algorithms Design Languages GPGPU programming Computational Framework HPC Stencil Computation

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Using Sample-Based Time Series Data for Automated Diagnosis of Scalability Losses in parallel Programs 20

Using Sample-Based Time Series Data for Automated Diagnosis ...

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25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Wei, Lai Mellor-Crummey, John Rice Univ Dept Comp Sci Houston TX 77005 USA Rice Univ Houston TX USA Pony Ai Fremont CA USA

ISBN: (纸本)9781450368186

the performance of many parallel applications has failed to scale as fast as successive generations of hardware on which these applications execute. To understand the cause of scalability losses, experts use performance tools to monitor and analyze application behavior. Profiles generated by performance tools can usually indicate the presence of scalability losses while time series data are generally necessary to pinpoint the root causes of such losses. However, manual analysis of time series data can be difficult in executions with a large number of processes, long running times, and deep call chains. this paper describes an automated framework that analyzes sample-based time series data to diagnose scalability losses in parallel executions. the framework's automated diagnosis of scalability losses indicates their symptoms, severity, and causes. Two case studies illustrate the effectiveness of this framework. When compared to a tool that analyzes performance using instrumentation-based traces, our overhead for collecting sample-based time series is 1/28 in time and 1/1600 in space while our automated analysis takes 1/25 of the time.

关键词： Performance automated diagnosis scalability losses sample-based time series data

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Copperhead: Compiling an Embedded Data parallel Language 11

Copperhead: Compiling an Embedded Data Parallel Language

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16th acm symposium on principles and practice of parallel programming

作者： Catanzaro, Bryan Garland, Michael Keutzer, Kurt Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781450301190

Modern parallel microprocessors deliver high performance on applications that expose substantial fine-grained data parallelism. Although data parallelism is widely available in many computations, implementing data parallel algorithms in low-level languages is often an unnecessarily difficult task. the characteristics of parallel microprocessors and the limitations of current programming methodologies motivate our design of Copperhead, a high-level data parallel language embedded in Python. the Copperhead programmer describes parallel computations via composition of familiar data parallel primitives supporting both flat and nested data parallel computation on arrays of data. Copperhead programs are expressed in a subset of the widely used Python programming language and interoperate with standard Python modules, including libraries for numeric computation, data visualization, and analysis. In this paper, we discuss the language, compiler, and runtime features that enable Copperhead to efficiently execute data parallel code. We define the restricted subset of Python which Copperhead supports and introduce the program analysis techniques necessary for compiling Copperhead code into efficient low-level implementations. We also outline the runtime support by which Copperhead programs interoperate with standard Python modules. We demonstrate the effectiveness of our techniques with several examples targeting the CUDA platform for parallel programming on GPUs. Copperhead code is concise, on average requiring 3.6 times fewer lines of code than CUDA, and the compiler generates efficient code, yielding 45-100% of the performance of hand-crafted, well optimized CUDA code.

关键词： Python Data parallelism GPU Algorithms Design Performance

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Hierarchical Memory Management for Mutable State 18

Hierarchical Memory Management for Mutable State

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23rd acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Guatto, Adrien Westrick, Sam Raghunathan, Ram Acar, Umut Fluet, Matthew Carnegie Mellon Univ Pittsburgh PA 15213 USA Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9781450349826

It is well known that modern functional programming languages are naturally amenable to parallel programming. Achieving efficient parallelism using functional languages, however, remains difficult. Perhaps the most important reason for this is their lack of support for efficient in-place updates, i.e., mutation, which is important for the implementation of both parallel algorithms and the run-time system services (e.g., schedulers and synchronization primitives) used to execute them. In this paper, we propose techniques for efficient mutation in parallel functional languages. To this end, we couple the memory manager with the thread scheduler to make reading and updating data allocated by nested threads efficient. We describe the key algorithms behind our technique, implement them in the MLton Standard ML compiler, and present an empirical evaluation. Our experiments show that the approach performs well, significantly improving efficiency over existing functional language implementations.

关键词： parallel functional language implementation garbage collection hierarchical heaps mutation promotion

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PPoPP 2013 - Proceedings of the 2013 acm sigplan symposium on principles and practice of parallel programming

PPoPP 2013 - Proceedings of the 2013 ACM SIGPLAN Symposium o...

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18th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2013

ISBN: (纸本)9781450319225

the proceedings contain 45 papers. the topics discussed include: a peta-scalable CPU-GPU algorithm for global atmospheric simulations;adoption protocols for fanout-optimal fault-tolerant termination detection;betweenness centrality: algorithms and implementations;complexity analysis and algorithm design for reorganizing data to minimize non-coalesced memory accesses on GPU;fast concurrent queues for x86 processors;FASTLANE: improving performance of software transactional memory for low thread counts;Ligra: a lightweight graph processing framework for shared memory;ownership passing: efficient distributed memory programming on multi-core systems;parallel suffix array and least common prefix for the GPU;Streamscan: fast scan algorithms for GPUs without global barrier synchronization;using hardware transactional memory to correct and simplify a readers-writer lock algorithm;and exploring different automata representations for efficient regular expression matching on GPUs.

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XIndex: A Scalable Learned Index for Multicore Data Storage 20

XIndex: A Scalable Learned Index for Multicore Data Storage

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25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Tang, Chuzhe Wang, Youyun Dong, Zhiyuan Hu, Gansen Wang, Zhaoguo Wang, Minjie Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Scalable Comp & Syst Shanghai Peoples R China NYU Dept Comp Sci New York NY 10003 USA

ISBN: (纸本)9781450368186

We present XIndex, a concurrent ordered index designed for fast queries. Similar to a recent proposal of the learned index, XIndex uses learned models to optimize index efficiency. Comparing with the learned index, XIndex is able to effectively handle concurrent writes without affecting the query performance by leveraging fine-grained synchronization and a new compaction scheme, Two-Phase Compaction. Furthermore, XIndex adapts its structure according to runtime workload characteristics to support dynamic workload. We demonstrate the advantages of XIndex with both YCSB and TPC-C (KV), a TPC-C variant for key-value stores. XIndex achieves up to 3.2x and 4.4x performance improvement comparing with Masstree and Wormhole, respectively, on a 24-core machine, and it is open-sourced(1).

关键词： Compaction

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