{"title":"Session details: Keynote Address","authors":"M. Gerndt","doi":"10.1145/3248632","DOIUrl":"https://doi.org/10.1145/3248632","url":null,"abstract":"","PeriodicalId":409042,"journal":{"name":"Proceedings of the ACM Workshop on Software Engineering Methods for Parallel and High Performance Applications","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115456935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modern emerging workloads such as analytics, graph, and deep learning, rapidly appear. These are written by non-Ninja programmers. Modern hardware platforms are becoming complex due to deployments of hardware accelerators such as GPGPU and FPGA. It is not easy for them to fully exploit these capabilities. Our recent challenges are to achieve high performance of these workloads. In this talk, I will review how hardware platform, workload, and software for high performance computation were changing. I will then think about what are better approaches for users to describe their problems with high productivity and performance. I will talk about technical approaches and challenges in these descriptions to exploit hardware capabilities. We need to think what information we should get and what optimizations we can do for future hardware system.
{"title":"Challenges in Transition","authors":"K. Ishizaki","doi":"10.1145/2916026.2916032","DOIUrl":"https://doi.org/10.1145/2916026.2916032","url":null,"abstract":"Modern emerging workloads such as analytics, graph, and deep learning, rapidly appear. These are written by non-Ninja programmers. Modern hardware platforms are becoming complex due to deployments of hardware accelerators such as GPGPU and FPGA. It is not easy for them to fully exploit these capabilities. Our recent challenges are to achieve high performance of these workloads. In this talk, I will review how hardware platform, workload, and software for high performance computation were changing. I will then think about what are better approaches for users to describe their problems with high productivity and performance. I will talk about technical approaches and challenges in these descriptions to exploit hardware capabilities. We need to think what information we should get and what optimizations we can do for future hardware system.","PeriodicalId":409042,"journal":{"name":"Proceedings of the ACM Workshop on Software Engineering Methods for Parallel and High Performance Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130224570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Sikora, Eduardo César, Isaías A. Comprés Ureña, M. Gerndt
The main problem when trying to optimize the parameters of libraries, such as MPI, is that there are many parameters that users can configure. Moreover, predicting the behavior of the library for each configuration is non-trivial. This makes it very difficult to select good values for these parameters. This paper proposes a model for autotuning MPI applications. The model is developed to analyze different parameter configurations and is expected to aid users to find the best performance for executing their applications. As part of the AutoTune project, our work is ultimately aiming at extending Periscope to apply automatic tuning to parallel applications. In particular, our objective is to provide a straightforward way of tuning MPI parallel codes. The output of the framework are tuning recommendations that can be integrated into the production version of the code. Experimental tests demonstrate that this methodology could lead to significant performance improvements.
{"title":"Autotuning of MPI Applications Using PTF","authors":"A. Sikora, Eduardo César, Isaías A. Comprés Ureña, M. Gerndt","doi":"10.1145/2916026.2916028","DOIUrl":"https://doi.org/10.1145/2916026.2916028","url":null,"abstract":"The main problem when trying to optimize the parameters of libraries, such as MPI, is that there are many parameters that users can configure. Moreover, predicting the behavior of the library for each configuration is non-trivial. This makes it very difficult to select good values for these parameters. This paper proposes a model for autotuning MPI applications. The model is developed to analyze different parameter configurations and is expected to aid users to find the best performance for executing their applications. As part of the AutoTune project, our work is ultimately aiming at extending Periscope to apply automatic tuning to parallel applications. In particular, our objective is to provide a straightforward way of tuning MPI parallel codes. The output of the framework are tuning recommendations that can be integrated into the production version of the code. Experimental tests demonstrate that this methodology could lead to significant performance improvements.","PeriodicalId":409042,"journal":{"name":"Proceedings of the ACM Workshop on Software Engineering Methods for Parallel and High Performance Applications","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130381399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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