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Genesis: a language for generating synthetic training programs for machine learning
Published: 06 May 2015 Publication History
CF '15: Proceedings of the 12th ACM International Conference on Computing Frontiers
CF '15
Paper Acceptance Rate 33 of 96 submissions, 34% Overall Acceptance Rate 186 of 501 submissions, 37%
Funding Sources Qualcomm Natural Sciences and Engineering Research Council of Canada
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We describe Genesis, a language for the generation of synthetic programs for use in machine learning-based performance auto-tuning. The language allows users to annotate a template program to customize its code using statistical distributions and to generate program instances based on those distributions. This effectively allows users to generate training programs whose characteristics or features vary in a statistically controlled fashion. We describe the language constructs, a prototype preprocessor for the language, and three case studies that show the ability of Genesis to express a range of training programs in different domains. We evaluate the preprocessor's performance and the statistical quality of the samples it generates. We believe that Genesis is a useful tool for generating large and diverse sets of programs, a necessary component when training machine learning models for auto-tuning.
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Istituto di Calcolo e Reti ad Alte Prestazioni, CNR, ITALY
Institute for Computing Technology, Chinese Academy of Sciences, PRC
Association for Computing Machinery
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Scaling application properties to exascale
Published: 06 May 2015 Publication History
CF '15: Proceedings of the 12th ACM International Conference on Computing Frontiers
CF '15
Paper Acceptance Rate 33 of 96 submissions, 34% Overall Acceptance Rate 186 of 501 submissions, 37%
Funding Sources Netherlands Organisation for Scientific Research (NWO) Dutch Ministry of EL&I Province of Drenthe
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Exascale computing systems will execute computationally intensive tasks on unprecedented amounts of data. Tuning the design of such systems for a specific application or for an application domain is a challenging task as it is not yet possible to analyze the actual run-time behavior of exascale applications. Run-time properties, such as the memory access pattern, the available instruction-level parallelism and the instruction mix, are valuable information for architects to tune the processing elements, the memory system and the communication infrastructure.
We propose a methodology for extrapolating application properties at exascale from an analysis of workload sizes feasible on current systems. The methodology is suitable for applications scaling over different parameters (e.g., the number of vertices and edges represent two parameters in a graph algorithm). The proposed methodology combines a) a statistically sound approach for model selection and b) knowledge coming from computational theory, such as the order of complexity of the application under analysis. Compared with state-of-the-art techniques, the proposed methodology reduces the prediction error by an order of magnitude on the instruction count and improves the accuracy of memory access pattern prediction by up to 1.3×.
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Istituto di Calcolo e Reti ad Alte Prestazioni, CNR, ITALY
Institute for Computing Technology, Chinese Academy of Sciences, PRC
Association for Computing Machinery
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https://dl.acm.org/doi/10.1145/2742854.2742860
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Enhanced GPU-based distributed breadth first search
Published: 06 May 2015 Publication History
CF '15: Proceedings of the 12th ACM International Conference on Computing Frontiers
CF '15
Paper Acceptance Rate 33 of 96 submissions, 34% Overall Acceptance Rate 186 of 501 submissions, 37%
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