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Energy-optimal configuration selection for manycore chips with variation

INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2017年第5期31卷 451-466页

作者： Langer, Akhil Totoni, Ehsan Palekar, Udatta Kale, Laxmikant V. Intel Fed 1906 Fox Dr Champaign IL 61820 USA Univ Illinois Coll Business Urbana IL 61801 USA Univ Illinois Dept Comp Sci 1304 W Springfield Ave Urbana IL 61801 USA

Operating chips at high energy efficiency is one of the major challenges for modern large-scale supercomputers. Low-voltage operation of transistors increases the energy efficiency but leads to frequency and power variation across cores on the same chip. Finding energy-optimal configurations for such chips is a hard problem. In this work, we study how integer linear programming techniques can be used to obtain energy-efficient configurations of chips that have heterogeneous cores. Our proposed methodologies give optimal configurations as compared with competent but sub-optimal heuristics while having negligible timing overhead. the proposed ParSearch method gives up to 13.2% and 7% savings in energy while causing only 2% increase in execution time of two HPC applications: miniMD and Jacobi, respectively. Our results show that integer linear programming can be a very powerful online method to obtain energy-optimal configurations.

关键词： energy power optimization multicore chips low-voltage computing near-threshold voltage computing process variation heterogeneity integer programming quadratic integer programming

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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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Reaching bandwidth saturation using transparent injection parallelization

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2017年第5期31卷 405-421页

作者： Chaimov, Nicholas Ibrahim, Khaled Z. Williams, Samuel Iancu, Costin Univ Oregon Eugene OR 97403 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA USA Lawrence Berkeley Natl Lab Performance & Algorithms Res Grp Berkeley CA USA

Although logically available, applications may not exploit enough instantaneous communication concurrency to maximize network utilization on HPC systems. this is exacerbated in hybrid programming models that combine single program multiple data with OpenMP or CUDA. We present the design of a multi-threaded runtime able to transparently increase the instantaneous network concurrency and to provide near saturation bandwidth, independent of the application configuration and dynamic behavior. the runtime offloads communication requests from application level tasks to multiple communication servers. the servers use system specific performance models to attain network saturation. Our techniques alleviate the need for spatial and temporal application level message concurrency optimizations. Experimental results show improved message throughput and bandwidth by as much as 150% for 4 KB messages on InfiniBand and by as much as 120% for 4 KB messages on Cray Aries. For more complex operations such as all-to-all collectives, we observe as much as 30% speedup. this translates into 23% speedup on 12,288 cores for a NAS FT implemented using FFTW. We observe as much as 76% speedup on 1500 cores for an already optimized UPC+OpenMP geometric multigrid application using hybrid parallelism. For the geometric multigrid GPU implementation, we observe as much as 44% speedup on 512 GPUs.

关键词： Communication concurrency multi-threaded runtime one sided communication parallel injection UPC

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JParEnt: parallel entropy decoding for JPEG decompression on heterogeneous multicore architectures 7

JParEnt: Parallel entropy decoding for JPEG decompression on...

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Euro-Par Conference / 7th International Workshop on programming Models and Applications for Multicores and Manycores (PMAM) held in conjunction with the 21st sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Sodsong, Wasuwee Jung, Minyoung Park, Jinwoo Burgstaller, Bernd Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9781450341967

the JPEG format employs Huffman codes to compress the entropy data of an image. Huffman codewords are of variable length, which makes parallel entropy decoding a difficult problem. To determine the start position of a codeword in the bitstream, the previous codeword must be decoded first. We present JParEnt, a new approach to parallel entropy decoding for JPEG decompression on heterogeneous multicores. JParEnt conducts JPEG decompression in two steps: (1)an efficient sequential scan of the entropy data on the CPU to determine the start-positions (boundaries) of coefficient blocks in the bitstream, followed by (2)a parallel entropy decoding step on the graphics processing unit (GPU). the block boundary scan constitutes a reinterpretation of the Huffman-coded entropy data to determine codeword boundaries in the bitstream. We introduce a dynamic workload partitioning scheme to account for GPUs of low compute power relative to the CPU. this configuration has become common with the advent of SoCs with integrated graphics processors (IGPs). We leverage additional parallelism through pipelined execution across CPU and GPU. For systems providing a unified address space between CPU and GPU, we employ zero-copy to completely eliminate the data transfer overhead. Our experimental evaluation of JParEnt was conducted on six heterogeneous multicore systems: one server and two desktops with dedicated GPUs, one desktop with an IGP, and two embedded systems. For a selection of more than 1000JPEG images, JParEnt outperforms the SIMD-implementation of the libjpeg-turbo library by up to a factor of 4.3x, and the previously fastest JPEG decompression method for heterogeneous multicores by up to a factor of 2.2x. JParEnt's entropy data scan consumes 45% of the entropy decoding time of libjpeg-turbo on average. Given this new ratio for the sequential part of JPEG decompression, JParEnt achieves up to97% of the maximum attainable speedup (95% on average). On the IGP-based desktop platform,

关键词： JPEG decoding entropy decoding prefix codes heterogeneous multicores GPU programming

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Efficient Massively parallel Methods for Dynamic programming 2017

Efficient Massively Parallel Methods for Dynamic Programming

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49th Annual acm-SIGACT symposium on theory of Computing (STOC)

作者： Im, Sungjin Moseley, Benjamin Sun, Xiaorui Univ Calif Elect Engn & Comp Sci 5200 N Lake Rd Merced CA 95344 USA Washington Univ Dept Comp Sci & Engn 1 Brookings Dr St Louis MO 63130 USA Univ Calif Berkeley Simons Inst Theory Comp Berkeley CA 94702 USA Simons Inst Theory Comp Berkeley CA USA

ISBN: (纸本)9781450345286

Modern science and engineering is driven by massively large data sets and its advance heavily relies on massively parallel computing platforms such as Spark, MapReduce, and Hadoop. theoretical models have been proposed to understand the power and limitations of such platforms. Recent study of developed theoretical models has led to the discovery of new algorithms that are fast and efficient in both theory and practice, thereby beginning to unlock their underlying power. Given recent promising results, the area has turned its focus on discovering widely applicable algorithmic techniques for solving problems efficiently. In this paper we make progress towards this goal by giving principles for simulating a large class of sequential dynamic programs in the distributed setting. In particular, we identify two key properties, monotonicity and decomposibility, which allow us to derive efficient distributed algorithms for problems possessing the properties. We showcase our framework by considering several core dynamic programming applications, Longest Increasing Subsequence, Optimal Binary Search Tree, and Weighted Interval Selection. For these problems, we derive algorithms yielding solutions that are arbitrarily close to the optimum, using O(1) rounds and (O) over tilde (n/m) memory on each machine where n is the input size and m is the number of machines available.

关键词： Massively parallel computing Dynamic programming

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Proceedings of the acm sigplan symposium on principles and practice of parallel programming, PPOPP

Proceedings of the ACM SIGPLAN Symposium on Principles and P...

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

ISBN: (纸本)9781450332057

the proceedings contain 44 papers. the topics discussed include: predicate RCU: an RCU for scalable concurrent updates;automatic scalable atomicity via semantic locking;a framework for practical parallel fast matrix multiplication;PLUTO+: near-complete modeling of affine transformations for parallelism and locality;distributed memory code generation for mixed irregular/regular computations;performance implications of dynamic memory allocators on transactional memory systems;low-overhead software transactional memory with progress guarantees and strong semantics∗;barrier elision for production parallel programs;scalable and efficient implementation of 3D unstructured meshes computation: a case study on matrix assembly;and diagnosing the causes and severity of one-sided message contention.

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Scalable adaptive NUMA-aware Lock combining local locking and remote locking for efficient concurrency 16

Scalable adaptive NUMA-aware Lock combining local locking an...

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21st acm sigplan symposium on principles and practice of parallel programming, PPoPP 2016

作者： Zhang, Mingzhe Lau, Francis C.M. Wang, Cho-Li Cheng, Luwei Chen, Haibo Dept. Computer Science University of Hong Kong Hong Kong Facebook United States Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781450340922

Scalable locking is a key building block for scalable multi-threaded software. Its performance is especially critical in multi-socket, multi-core machines with non-uniform memory access (NUMA). Previous schemes such as local locking and remote locking only perform well under a certain level of contention, and often require non-trivial tuning for a particular configuration. Besides, for large NUMA systems, because of unmanaged lock server's nomination, current distance-first NUMA policies cannot perform satisfactorily. In this work, we propose SANL, a locking scheme that can de-liver high performance under various contention levels by adap-tively switching between the local and the remote lock scheme. Furthermore, we introduce a new NUMA policy for the remote lock that jointly considers node distances and server utilization when choosing lock servers. A comparison with seven represen-tative locking schemes shows that SANL outperforms the others in most contention situations. In one group test, SANL is 3.7 times faster than RCL lock and 17 times faster than POSIX mutex. © 2016 acm.

关键词： Locks (fasteners)

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PPoPP 2014 - Proceedings of the 2014 acm sigplan symposium on principles and practice of parallel programming

PPoPP 2014 - Proceedings of the 2014 ACM SIGPLAN Symposium o...

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2014 19th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2014

ISBN: (纸本)9781450326568

the proceedings contain 43 papers. the topics discussed include: predator: predictive false sharing detection;concurrency testing using schedule bounding: an empirical study;trace driven dynamic deadlock detection and reproduction;efficient search for inputs causing high floating-point errors;portable, MPI-interoperable coarray Fortran;eliminating global interpreter locks in ruby through hardware transactional memory;leveraging hardware message passing for efficient thread synchronization;well-structured futures and cache locality;time-warp: lightweight abort minimization in transactional memory;beyond parallel programming with domain specific languages;a decomposition for in-place matrix transposition;in-place transposition of rectangular matrices on accelerators;and parallelizing dynamic programming through rank convergence.

关键词： FORTRAN (programming language)

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Tiles: A new language mechanism for heterogeneous parallelism 2015

Tiles: A new language mechanism for heterogeneous parallelis...

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

作者： Chen, Yifeng Cui, Xiang Mei, Hong HCST Key Lab. School of EECS Peking University Beijing100871 China

ISBN: (纸本)9781450332057

this paper studies the essence of heterogeneity from the perspective of language mechanism design. the proposed mechanism, called tiles, is a program construct that bridges two relative levels of computation: an outer level of source data in larger, slower or more distributed memory and an inner level of data blocks in smaller, faster or more localized memory.

关键词： parallel programming

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An OpenACC-based unified programming model for multi-accelerator systems 2015

An OpenACC-based unified programming model for multi-acceler...

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

作者： Kim, Jungwon Lee, Seyong Vetter, Jeffrey S. Oak Ridge National Laboratory United States Georgia Institute of Technology United States

ISBN: (纸本)9781450332057

this paper proposes a novel SPMD programming model of OpenACC. Our model integrates the different granularities of parallelism from vector-level parallelism to node-level parallelism into a single, unified model based on OpenACC. It allows programmers to write programs for multiple accelerators using a uniform programming model whether they are in shared or distributed memory systems. We implement a prototype of our model and evaluate its performance with a GPU-based supercomputer using three benchmark applications.

关键词： Supercomputers

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