2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)最新文献

{"title":"ScaDL 2022 Invited Talk 2: AI/ML Pipelines using CodeFlare","authors":"M. Srivatsa","doi":"10.1109/IPDPSW55747.2022.00167","DOIUrl":"https://doi.org/10.1109/IPDPSW55747.2022.00167","url":null,"abstract":"Pipelines have become a ubiquitous construct in machine learning spanning tasks ranging from data cleaning and preprocessing, training foundational models, model optimization and transfer learning and low latency inferencing. While the many pipeline construct has existed for many years (e.g., SciKit learn pipelines, Spark pipelines), this talk will focus on a process calculus style definition of pipeline - called CodeFlare pipelines - that makes it readily amenable to scaling complex AI/ML workflows on a commodity cluster. CodeFlare pipelines not only enable data scientists to introduce compute, data and multi-stage parallelism using simple annotations on the pipeline graph, but also operationalize them on a hybrid cloud platform (Red Hat OpenShift), thereby making the solution deployable just about anywhere and leverage the benefits of serverless computing. This talk will cover a basic realization of CodeFlare pipelines on the Ray platform (1.7.0 release) that has shown near linear scalability for transfer learning and inferencing on foundational models.","PeriodicalId":286968,"journal":{"name":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115128911","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}

{"title":"Fast Genome Analysis Leveraging Exact String Matching","authors":"Beatrice Branchini, Sofia Breschi, Alberto Zeni, M. Santambrogio","doi":"10.1109/IPDPSW55747.2022.00032","DOIUrl":"https://doi.org/10.1109/IPDPSW55747.2022.00032","url":null,"abstract":"Genome assembly is one of the most challenging tasks in bioinformatics, as it is the key to many applications. One of the fundamental tasks in genome assembly is exact sequence alignment. This process enables the identification of recurrent patterns and mutations inside the DNA, which can substantially support clinicians in providing a quicker diagnosis and producing individual-specific drugs. However, this procedure represents a bottleneck in genome analysis as it is computationally intensive and time-consuming. In this scenario, the efficiency of the chosen algorithm to perform this operation also plays a crucial role to speed up the analysis process. In this paper, we present a high-performance, energy-efficient FPGA implementation of the Knuth Morris Pratt (KMP) algorithm. Our multi-core architecture can parallelize the alignment procedure of the sequences, significantly reducing the execution time while still maintaining high flexibility. Experimental results show that our implementation on a Xilinx Alveo U280 achieves up to $2.68times$ speedup and up to $7.46times$ improvement in energy efficiency against Bowtie2, a State-of-the-Art application for sequence alignment run on a 40-thread Intel Xeon processor. Finally, our design also outperforms hardware-accelerated applications of the KMP present the State of the Art by up to $19.38times$ and $15.63times$ in terms of throughput and energy efficiency respectively.","PeriodicalId":286968,"journal":{"name":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127500423","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}

{"title":"EDAML 2022 Invited Speaker 1: Application of Machine Learning in High Level Synthesis","authors":"Ankush Sood","doi":"10.1109/IPDPSW55747.2022.00194","DOIUrl":"https://doi.org/10.1109/IPDPSW55747.2022.00194","url":null,"abstract":"Traditionally high-level optimizations like CSA and sharing have been done technology independent. Doing word level optimizations PPA aware require accurate models for power, timing and area and that needs mapping to technology library and iterations which are runtime intensive and not suitable for multimillion instance designs. In this talk, we discuss how machine learning could help make the right power/area/delay tradeoffs early in the synthesis flow not sacrificing on turnaround time.","PeriodicalId":286968,"journal":{"name":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125521112","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}

{"title":"Implementating Spatio-Temporal Graph Convolutional Networks on Graphcore IPUs","authors":"Johannes Moe, Konstantin Pogorelov, Daniel Thilo Schroeder, J. Langguth","doi":"10.1109/IPDPSW55747.2022.00016","DOIUrl":"https://doi.org/10.1109/IPDPSW55747.2022.00016","url":null,"abstract":"Artificial neural networks have been used for a multitude of regression tasks, and their descendants have expanded the domain to many applications such as image and speech recognition, filtering of social networks, and machine translation. While conventional and recurrent neural networks work well on data represented in Euclidean space, they struggle with data in non-Euclidean space. Graph Neural Networks (GNN) expand recurrent neural networks to directly process sparse representations of graphs, but they are computationally expensive, which invites the use of powerful hardware accelerators. In this paper, we investigate the viability of the Graphcore Intelligence Processing Unit (IPU) for efficient implementation of Spatio-Temporal Graph Convolutional Networks. The results show that IPUs are well suited for this task.","PeriodicalId":286968,"journal":{"name":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124644084","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}

{"title":"Workshop on Resource Arbitration for Dynamic Runtimes (RADR)","authors":"P. Beckman, E. Jeannot, Swann Perarnau","doi":"10.1109/IPDPSW55747.2022.00161","DOIUrl":"https://doi.org/10.1109/IPDPSW55747.2022.00161","url":null,"abstract":"The question of efficient dynamic allocation of compute-node resources, such as cores, by independent libraries or runtime systems can be a nightmare. Scientists writing application components have no way to efficiently specify and compose resource-hungry components. As application software stacks become deeper and the interaction of multiple runtime layers compete for resources from the operating system, it has become clear that intelligent cooperation is needed. Resources such as compute cores, in-package memory, and even electrical power must be orchestrated dynamically across application components, with the ability to query each other and respond appropriately. A more integrated solution would reduce intra-application resource competition and improve performance. Furthermore, application runtime systems could request and allocate specific hardware assets and adjust runtime tuning parameters up and down the software stack. The goal of this workshop is to gather and share the latest scholarly research from the community working on these issues, at all levels of the HPC software stack. This includes thread allocation, resource arbitration and management, containers, and so on, from runtime-system designers to compilers. We will also use panel sessions and keynote talks to discuss these issues, share visions, and present solutions.","PeriodicalId":286968,"journal":{"name":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129488261","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}

{"title":"21st IEEE International Workshop on High Performance Computational Biology (HiCOMB 2022)","authors":"","doi":"10.1109/IPDPSW55747.2022.00033","DOIUrl":"https://doi.org/10.1109/IPDPSW55747.2022.00033","url":null,"abstract":"","PeriodicalId":286968,"journal":{"name":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130384443","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}

{"title":"An Efficient Parallel Implementation of a Perfect Hashing Method for Hypergraphs","authors":"Somesh Singh, B. Uçar","doi":"10.1109/IPDPSW55747.2022.00056","DOIUrl":"https://doi.org/10.1109/IPDPSW55747.2022.00056","url":null,"abstract":"Querying the existence of an edge in a given graph or hypergraph is a building block in several algorithms. Hashing-based methods can be used for this purpose, where the given edges are stored in a hash table in a preprocessing step, and then the queries are answered using the lookup operations. While the general hashing methods have fast lookup times in the average case, the worst case run time is much higher. Perfect hashing methods take advantage of the fact that the items to be stored are all available and construct a collision free hash function for the given input, resulting in an optimal lookup time even in the worst case. We investigate an efficient shared-memory parallel implementation of a recently proposed perfect hashing method for hypergraphs. We experimentally compare the resulting parallel algorithms with the state-of-the-art and demonstrate better run time and scalability on a set of hypergraphs corresponding to real-life sparse tensors.","PeriodicalId":286968,"journal":{"name":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129192088","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}

{"title":"Feedback from a data center for education at CentraleSupélec engineering school","authors":"Jérémy Fix, S. Vialle, Rémi Hellequin, Claudine Mercier, P. Mercier, Jean-Baptiste Tavernier","doi":"10.1109/IPDPSW55747.2022.00065","DOIUrl":"https://doi.org/10.1109/IPDPSW55747.2022.00065","url":null,"abstract":"This paper describes the hardware and software architecture of the Data Center for Education (DCE) built up at CentraleSupélec as well as our educational objectives. Students experiment in the context of their studies with clusters of CPU and servers of GPU. They have different technical backgrounds from beginners to experts in computer science. The lectures have also different experimental requirements: from single host long running jobs for machine learning to multiple hosts short running HPC algorithms benchmarks, with lab works and projects that need to share the same pool of machines.","PeriodicalId":286968,"journal":{"name":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121283812","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}

{"title":"An Analysis of Mapping Polybench Kernels to HPC CGRAs","authors":"M. Weinhardt","doi":"10.1109/IPDPSW55747.2022.00114","DOIUrl":"https://doi.org/10.1109/IPDPSW55747.2022.00114","url":null,"abstract":"This paper presents a detailed analysis of Mapping the Polybench C 4.2.1 kernels to Coarse-Grain Reconfigurable Arrays (CGRAs), targeting High-Performance Computing (HPC). The results show that the Polybench kernels are well suited for acceleration on a CGRA due to their regular array accesses. However, seperately mapping the innermost loops of the Polybench kernels to a CGRA yields only limited speedups because the small size of the generated dataflow graphs limits the available parallelism and results in a low computational intensity. Therefore, loop transformations which will increase the parallelism and the speedups are suggested. While this work focuses on a specific CGRA and its compiler, the observations and conclusions are also transferable to other CGRAs and their compilers.","PeriodicalId":286968,"journal":{"name":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114314987","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}

{"title":"6th IEEE Workshop on Parallel and Distributed Processing for Computational Social Systems (ParSocial 2022)","authors":"John Korah, Eunice E. Santos","doi":"10.1109/IPDPSW55747.2022.00185","DOIUrl":"https://doi.org/10.1109/IPDPSW55747.2022.00185","url":null,"abstract":"Welcome to the Sixth IEEE Workshop on Parallel and Distributed Processing for Computational Social Systems (ParSocial 2022). This year the workshop highlights novel algorithms and models that leverage parallel computing with applications in social network and social media analysis. The first set of papers focus on the key individual identification problem in social network analysis. The paper by Vandromme et al entitled “Efficient Parallel PageRank Algorithm for Network Analysis” proposes a more efficient parallel algorithm for PageRank that has been shown to improve the time complexity by a factor of two. In a similar vein, the paper by Sahu et al entitled “Dynamic Batch Parallel Algorithms for Updating PageRank” proposes two parallel algorithms for recomputing PageRank of nodes in a dynamic social network that can scale across various architectures. A related research problem is identifying opinion leaders that can improve information dissemination within communities. The paper entitled “Effect of Community-based Opinion Leaders on Guideline Dissemination in Large-Scale Physician Networks” by Murugappan et al, focuses on the problem of the dissemination of medical guidelines. The authors propose a culturally infused agent based model to analyze the effectiveness of various opinion leader selection strategies and the tradeoffs between the reach and rate of spread of medical guideline information. The next set of papers focus on social media analysis. Systems for large scale ingestion of social media data sets can support a wide range of research problems in computational social systems. A step in this direction is taken by authors Huber et al, who have proposed a parallel system for large scale processing of Reddit data in their paper entitled “A Streaming System for Large-scale Mining of Reddit Data”. On the other hand, authors Abeysinghe et al in their short research paper entitled “Unsupervised User Stance Detection on Tweets Against Web Articles Using Sentence Transformers”, have proposed a parallel computing based technique to identify the stance of users using the information and articles shared in their tweets. Finally, the short research paper by Bogle et al entitled “Distributed Algorithms for the Graph Biconnectivity and Least Common Ancestor Problems” focuses on the problem of connectivity in social networks and tackles the problem of identification of cut vertices and edges in networks by formulating a parallel biconnectivity algorithm for distributed graph structures.","PeriodicalId":286968,"journal":{"name":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114752113","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}