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Optimizing modulo scheduling to achieve reuse and concurrency for stream processors

JOURNAL OF SUPERCOMPUTING 2012年第3期59卷 1229-1251页

作者： Wang, Li Xue, Jingling Yang, Xuejun Natl Univ Def Technol Sch Comp Changsha 410073 Hunan Peoples R China Univ New S Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

Both reuse and concurrency are performance-critical for stream processors. When applying loop unrolling and software pipelining separately to stream-level loops, either reuse or concurrency or both may be inadequately exploited. In this paper, we optimize modulo scheduling to maximize stream reuse and improve concurrency for stream-level loops. The key insight is that an unrolled and software-pipelined stream-level loop could be described by a set of reuse equations. Guided by reuse equations, a reuse-aware modulo scheduling algorithm is developed to simultaneously optimize the two performance objectives, reuse, and concurrency, for a loop in a unified framework. Moreover, we describe a code generation algorithm to automatically produce the optimized loop from a given loop. The experimental results obtained on FT64 and by simulation demonstrate the effectiveness of the proposed approach.

关键词： stream processor stream programming model stream register file Loop unrolling Software pipelining

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Exploiting the Reuse Supplied by Loop-Dependent stream References for stream Processors

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ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION 2010年第2期7卷 1–35页

作者： Yang, Xuejun Zhang, Ying Lu, Xicheng Xue, Jingling Rogers, Ian Li, Gen Wang, Guibin Fang, Xudong Univ New S Wales Sydeny Australia Univ Manchester Manchester Lancs England Natl Univ Def Technol Sch Comp Changsha Hunan Peoples R China

Memory accesses limit the performance of stream processors. By exploiting the reuse of data held in the stream Register File (SRF), an on-chip, software controlled storage, the number of memory accesses can be reduced. In current stream compilers, reuse exploitation is only attempted for simple stream references, those whose start and end are known. Compiler analysis, from outside of stream processors, does not directly enable the consideration of other more complex stream references. In this article, we propose a transformation to automatically optimize stream programs to exploit the reuse supplied by loop-dependent stream references. The transformation is based on three results: lemmas identifying the reuse supplied by stream references, a new abstract representation called the stream Reuse Graph (SRG) depicting the identified reuse, and the optimization of the SRG for our transformation. Both the reuse between the whole sequences accessed by stream references and between partial sequences is exploited in the article. In particular, partial reuse and its treatment are quite new and have never, to the best of our knowledge, appeared in scalar and vector processing. At the same time, reusing streams increases the pressure on the SRF, and this presents a problem of which reuse should be exploited within limited SRF capacity. We extend our analysis to achieve this objective. Finally, we implement our techniques based on the streamC/KernelC compiler that has been optimized with the best existing compilation techniques for stream processors. Experimental results show a resultant speed-up of 1.14 to 2.54 times using a range of benchmarks.

关键词： Algorithms Design Management Performance Experimentation stream programming model stream professor stream register file streamc stream reuse

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The Implementation and Analysis of Important Symmetric Ciphers on stream Processor

The Implementation and Analysis of Important Symmetric Ciphe...

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International Conference on Computer Engineering and Applications（ICCEA 2009）

作者： Ping Yao 1,Mu Xu 1,Gu Liu 1,Guang Xu1,Hong An1 2,Wenting Han1 1Department of Computer Science and Technology,University of Science and Technology of China,Hefei,230027,China 2 Key Laboratory of Computer System and Architecture,Chinese Academy of Sciences,Beijing,100080,China

Imagine is a stream processor that employs a two-level register hierarchy with 9.7 Kbytes of local register file capacity and 128 Kbytes of stream register file(SRF) capacity to capture producerconsumer locality in stream *** is exploited using an array of 48 floating-point arithmetic units organized as eight SIMD clusters with a 6-wide VLIW per *** achieves good performance in multimedia applications,signal processing,and scientific computing,but how it works in information security area remains *** paper implements Base64 and several important symmetric ciphers on Imagine,including Blowfish,Rijndael and *** result shows a speed up of 3x,2.4x,2.5x,and 5x over traditional implementations *** shows good potential in information security *** comparing the performances achieved by Imagine and general purpose processor and analyzing the characteristics of applications,we also propose a checking model for choosing applications which can get better performance on *** implying this model,we can find whether an application is suitable for Imagine,rather than implementing it on Imagine and then check the performance,which would waste much time.

关键词： stream Processing stream programming model Symmetric Cipher Imagine

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Exploiting Loop-Dependent stream Reuse for stream Processors

Exploiting Loop-Dependent Stream Reuse for Stream Processors

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17th International Conference on Parallel Architectures and Compilation Techniques

作者： Yang, Xuejun Zhang, Ying Xue, Jingling Rogers, Ian Li, Gen Wang, Guibin Natl Univ Def Technol Sch Comp Changsha Peoples R China

ISBN: (纸本)9781605582825

The memory access limits the performance of stream processors. By exploiting the reuse of data held in the stream Register File (SRF), an on-chip storage, the number of memory accesses can be reduced. In current stream compilers reuse is only attempted for simple stream references, those whose start and end are known. Compiler analysis from outside of stream processors does not directly enable the consideration of other complex stream references. In this paper we propose a transformation to automatically optimize stream programs to exploit the reuse supplied by loop-dependent stream references. The transformation is based on three results: algorithms to recognize the reuse supplied by stream references, a new abstract expression called the stream Reuse Graph (SRG) to depict the reuse and the optimization of the SRG for the transformation. Both the reuse between whole sequences accessed by stream references and that between partial sequences are exploited in the paper. In particular, the problem of exploiting partial stream reuse does not have its parallel in the traditional data reuse exploitation setting (for scalars and arrays). Finally, we have implemented our techniques using the streamC/KernelC compiler for Imagine. Experimental results show a resultant speedup of 1.14 to 2.54 times using a range of typical stream processing application kernels.

关键词： stream programming model stream Professor stream Register File streamC stream Reuse

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Optimizing stream organization to improve the performance of scientific computing applications on the stream processor

Optimizing stream organization to improve the performance of...

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7th International Conference on Algorithms and Architectures for Parallel Processing

作者： Zhang, Ying Li, Gen Yang, Xuejun Zeng, Kun Natl Univ Def Technol Inst Comp Changsha 410073 Peoples R China

ISBN: (纸本)9783540729044

It is very important to organize streams well to make stream programs take advantage of the parallel computing and memory system of the stream processor effectively, especially for scientific stream programs. In this paper, after analyzing typical scientific programs, we present and characterize two methods to optimize the stream organization: stream reusing and stream transpose. Several representative scientific stream programs with and without our optimization are performed on a stream typical processor simulator. Simulation results show that these methods can improve scientific stream program performance greatly.

关键词： scientific computing stream programming model stream processor cluster SIMD parallel computing stream reusing stream transpose inter-cluster communication

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