A Speed-up Technique for an Auto-Memoization Processor by Reusing Partial Results of Instruction Regions

Kamimura, Kazutaka; Oda, Ryosuke; Yamada, T.; Tsumura, Tomoaki; Matsuo, Hiroshi; Nakashima, Yasuhiko

doi:10.1109/icnc.2012.17

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…Therefore, in future, we will have to implement the mechanism for avoiding memoizing unsuitable computation blocks. This mechanism which is called overhead filter have been already implemented on the SPARC-based Auto-Memoization Processor [2], and we know that the performance degradation by increasing the search overhead can be restrained with this mechanism.…”

Section: B Evaluation With Spec Cpu95mentioning

confidence: 99%

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An implementation of Auto-Memoization mechanism on ARM-based superscalar processor

Shibata

Tsumura

et al. 2014

2014 International Symposium on System-on-Chip (SoC)

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We have proposed a processor called Auto Memoization Processor which is based on computation reuse. Un til now, we have implemented the auto-memoization mechanism on a single-issue non-pipelined SPARC processor and studied the processor. The processor dynamically detects functions and loop iterations as reusable blocks, and memoizes them automatically.In addition, the processor can apply computation reuse to the blocks with a little reuse overhead. However, the fine evaluation result of the processor may not guarantee enough practicality. This is because instead of such a simple architecture, superscalar architectures are now widely used for generic processors for PCs, embedded processors, and other various processors. Hence, we examine problems which will be caused in the case of implementing the auto-memoization mechanism on an ARM based superscalar processor and design the ARM-based Auto Memoization Processor. For example, one of such problems is that pipeline stalls are caused because of the reuse overhead. To solve this problem, we implement a mechanism for overlapping the reuse overhead and the pipeline execution of the processor. The evaluation result with SPEC CPU95 benchmark suite shows that the ARM-based Auto-Memoization Processor can also achieve speed-up as well as the previous SPARC-based Auto-Memoization Processor. In this paper, we describe the implementation and the evaluation result of the ARM-based Auto-Memoization Processor.

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Section: B Evaluation With Spec Cpu95mentioning

confidence: 99%

“…Meanwhile, we have proposed a processor called Auto Memoization Processor based on computation reuse [1] [2]. Auto-Memoization Processor dynamically detects functions and loop iterations as reusable blocks, and memoizes them automatically.…”

Section: Introductionmentioning

confidence: 99%

An implementation of Auto-Memoization mechanism on ARM-based superscalar processor

Shibata

Tsumura

et al. 2014

2014 International Symposium on System-on-Chip (SoC)

Self Cite

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“…Literature 6 provides a clear distinction between them. But when we compare CR [7][8][9][10][11][12][13][14][15][16]30 and MZ, [17][18][19][20][21][22][23][24][25][26][27][28][29]35,36 we could not found sufficient difference among these two. Conceptually, CR refers to "reusing previously computed (buffered) results if same input appears again," and MZ refers to "storing the current result and reuse in future if similar input appears again.…”

Section: Prior Workmentioning

confidence: 99%

Approximate function memoization

Arundhati

Jena

Pani

2022

Concurrency and Computation

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Summary Function memoization is an optimization technique that reduces a function call overhead when the same input appears again. A table that stores the previous result is searched and used to skip the repeated computation. This way, it increases the performance of the function call. In this article, we propose a software approach of function memoization to improve computing efficiency by bypassing the execution of the function implemented using approximate computing techniques. Searching overhead is a primary concern in any memoization technique proposed so far. In traditional function memoization, the input arguments are first searched in the look‐up table (LUT) for an exact match, and the corresponding result is extracted for further use. But, in this article, a decision‐making rule is proposed to help us decide whether to search the LUT or go for the actual computation. This decision‐making model is implemented through Bloom filter and Cantor's pairing function. Because Bloom filter sometimes produces false‐positive results, we suggest a simple approximation technique that searches the LUT for an approximate match rather than an exact match. The proposed model also contains a bypass algorithm implemented through C++ code that identifies the trivial computations from the input argument of the candidate function. By this, we can avoid the actual calculation and generate the result directly. Here, trivial computation identifies one or more input arguments that are either 0 or prefix±1$$ \pm 1 $$. To analyze the effectiveness of our proposed technique, we conducted several experiments using the benchmarks from the AxBench suite. We found that our result outperforms some of the methods proposed so far in terms of energy consumption and quality of results, particularly in image processing applications.

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“…The memoization can be applied at different levels: (a) In Hardware, for reusing partial results on instruction blocks [7] or at CPU instruction level for arithmetic operations [8]. (b) In software, like in logic programming in order to 'learn' intermediated results as predicates (Page 182 of [9] explain it for Prolog systems), or in functional programming [10] even with concurrency and communication.…”

Section: Related Workmentioning

confidence: 99%

Improving MPI applications with a new MPI_Info and the use of the memoization

Calderón

Carretero

Garcı́a-Carballeira

et al. 2013

Proceedings of the 20th European MPI Users' Group Meeting

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The MPI forum is actively working for a better MPI standard. The results are the new version 3 of the MPI standard, and the efforts for the incoming MPI 3.1/4.0. The technological changes provide many opportunities for improvements and new ideas. This paper introduces two main contributions in this direction: (1) how to improve the MPI Info object implementation, and (2) a new way of using the former improved MPI Info object as a storage solution.The MPI Info object [1] is described as an unordered set of key-value pairs (both key and value are strings, and keys are unique). And it is implemented as a linked list in the two major MPI implementations available.We propose a new MPI Info object implementation that (1) is based on the use of hash tables (what improves the overall performance), and (2) abstracts the underlying hash table infrastructure (what facilitates the usage of the most appropriated solution). Our proposal opens the possibility of extending the use of the MPI Info object as a shared storage solution among MPI processes. To demonstrate the capabilities of our proposal, we explore the utilization of the memoization technique on MPI applications in order to improve the execution performance.

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A Speed-up Technique for an Auto-Memoization Processor by Reusing Partial Results of Instruction Regions

Cited by 10 publications

References 16 publications

An implementation of Auto-Memoization mechanism on ARM-based superscalar processor

An implementation of Auto-Memoization mechanism on ARM-based superscalar processor

Approximate function memoization

Improving MPI applications with a new MPI_Info and the use of the memoization

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