Parallel ComputingPub Date : 2023-09-01Epub Date: 2023-07-14DOI: 10.1016/j.parco.2023.103037
Ami Marowka , Przemysław Stpiczyński
{"title":"Editorial on Advances in High Performance Programming","authors":"Ami Marowka , Przemysław Stpiczyński","doi":"10.1016/j.parco.2023.103037","DOIUrl":"https://doi.org/10.1016/j.parco.2023.103037","url":null,"abstract":"","PeriodicalId":54642,"journal":{"name":"Parallel Computing","volume":"117 ","pages":"Article 103037"},"PeriodicalIF":1.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49877858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Parallel ComputingPub Date : 2023-09-01Epub Date: 2023-06-14DOI: 10.1016/j.parco.2023.103034
Bin Yu , Xu Lu , Cong Tian , Meng Wang , Chu Chen , Ming Lei , Zhenhua Duan
{"title":"Adaptively parallel runtime verification based on distributed network for temporal properties","authors":"Bin Yu , Xu Lu , Cong Tian , Meng Wang , Chu Chen , Ming Lei , Zhenhua Duan","doi":"10.1016/j.parco.2023.103034","DOIUrl":"https://doi.org/10.1016/j.parco.2023.103034","url":null,"abstract":"<div><p>Runtime verification<span><span> is a lightweight verification technique that verifies whether a monitored program execution satisfies a desired property. Online runtime verification faces challenges regarding efficiency and property expressiveness, which limit its widespread adoption. However, there is a lack of research that addresses both of these issues. With the basis of a distributed network, we propose an adaptively parallel approach to verify full regular temporal properties of C programs in an online manner. During program execution, segments of the generated state sequence are verified by distributed machines concurrently, while each segment is also verified in each multi-core machine with an adaptive number of </span>threads. Experimental results demonstrate that, with supporting more expressive properties, our approach has a speedup of 2.5X–5.0X compared with other runtime verification approaches.</span></p></div>","PeriodicalId":54642,"journal":{"name":"Parallel Computing","volume":"117 ","pages":"Article 103034"},"PeriodicalIF":1.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49877448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using heterogeneous GPU nodes with a Cabana-based implementation of MPCD","authors":"R. Halver, Christoph Junghans, G. Sutmann","doi":"10.1016/j.parco.2023.103033","DOIUrl":"https://doi.org/10.1016/j.parco.2023.103033","url":null,"abstract":"","PeriodicalId":54642,"journal":{"name":"Parallel Computing","volume":"117 1","pages":"103033"},"PeriodicalIF":1.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"55107193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Parallel ComputingPub Date : 2023-09-01Epub Date: 2023-06-02DOI: 10.1016/j.parco.2023.103025
Srđan Daniel Simić, Nikola Tanković, Darko Etinger
{"title":"Big data BPMN workflow resource optimization in the cloud","authors":"Srđan Daniel Simić, Nikola Tanković, Darko Etinger","doi":"10.1016/j.parco.2023.103025","DOIUrl":"https://doi.org/10.1016/j.parco.2023.103025","url":null,"abstract":"<div><p>Cloud computing is one of the critical technologies that meet the demand of various businesses for the high-capacity computational processing power needed to gain knowledge from their ever-growing business data. When utilizing cloud computing resources to deal with Big Data processing, companies face the challenge of determining the optimal use of resources within their business processes. The miscalculation of the necessary resources directly affects their budget and can cause delays in the cycle time of their key processes. This study investigates the simulation of cloud resource optimization for Big Data workflows modeled with the Business Process Modeling Notation (BPMN). To this end, a BPMN performance evaluation framework was developed. The framework’s capabilities were presented using real-world data science workflow and later evaluated on workflows consisting of 13, 52, and 104 tasks. The results show that the developed framework is adequate for estimating the overall run-time distribution and optimizing the cloud resource deployment and that the BPMN can be utilized for Big Data processing workflows. Therefore, this study contributes to BPMN practitioners by providing a tool to apply BPMN for their Big Data workflows and decision-makers by giving them critical insights into their key business processes. The framework source code is available at <span>https://github.com/ntankovic/python-bpmn-engine</span><svg><path></path></svg>.</p></div>","PeriodicalId":54642,"journal":{"name":"Parallel Computing","volume":"117 ","pages":"Article 103025"},"PeriodicalIF":1.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49877447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization","authors":"I. Laguna, Anh Tran, G. Gopalakrishnan","doi":"10.1016/j.parco.2023.103042","DOIUrl":"https://doi.org/10.1016/j.parco.2023.103042","url":null,"abstract":"","PeriodicalId":54642,"journal":{"name":"Parallel Computing","volume":"62 1","pages":"103042"},"PeriodicalIF":1.4,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"55107870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A flexible sparse matrix data format and parallel algorithms for the assembly of finite element matrices on shared memory systems","authors":"A. Sky, César Polindara, I. Muench, C. Birk","doi":"10.1016/j.parco.2023.103039","DOIUrl":"https://doi.org/10.1016/j.parco.2023.103039","url":null,"abstract":"","PeriodicalId":54642,"journal":{"name":"Parallel Computing","volume":"117 1","pages":"103039"},"PeriodicalIF":1.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"55107767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Parallel ComputingPub Date : 2023-07-01Epub Date: 2023-04-14DOI: 10.1016/j.parco.2023.103021
Shelby Lockhart , Amanda Bienz , William D. Gropp , Luke N. Olson
{"title":"Characterizing the performance of node-aware strategies for irregular point-to-point communication on heterogeneous architectures","authors":"Shelby Lockhart , Amanda Bienz , William D. Gropp , Luke N. Olson","doi":"10.1016/j.parco.2023.103021","DOIUrl":"https://doi.org/10.1016/j.parco.2023.103021","url":null,"abstract":"<div><p>Supercomputer architectures are trending toward higher computational throughput due to the inclusion of heterogeneous compute nodes. These multi-GPU nodes increase on-node computational efficiency, while also increasing the amount of data to be communicated and the number of potential data flow paths. In this work, we characterize the performance of irregular point-to-point communication with MPI on heterogeneous compute environments through performance modeling, demonstrating the limitations of standard communication strategies for both device-aware and staging-through-host communication techniques. Presented models suggest staging communicated data through host processes then using node-aware communication strategies for high inter-node message counts. Notably, the models also predict that node-aware communication utilizing all available CPU cores to communicate inter-node data leads to the most performant strategy when communicating with a high number of nodes. Model validation is provided via a case study of irregular point-to-point communication patterns in distributed sparse matrix–vector products. Importantly, we include a discussion on the implications model predictions have on communication strategy design for emerging supercomputer architectures.</p></div>","PeriodicalId":54642,"journal":{"name":"Parallel Computing","volume":"116 ","pages":"Article 103021"},"PeriodicalIF":1.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49728377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Parallel ComputingPub Date : 2023-07-01Epub Date: 2023-03-09DOI: 10.1016/j.parco.2023.103018
Akira Nukada , Taichiro Suzuki , Satoshi Matsuoka
{"title":"Efficient checkpoint/Restart of CUDA applications","authors":"Akira Nukada , Taichiro Suzuki , Satoshi Matsuoka","doi":"10.1016/j.parco.2023.103018","DOIUrl":"https://doi.org/10.1016/j.parco.2023.103018","url":null,"abstract":"<div><p>We present NVCR<span> which enables transparent checkpoint and restart of CUDA applications. NVCR, works as an extension of major system-level checkpoint software such as BLCR and DMTCP, employs proxy-process and application accesses GPU devices via the proxy-process to improve the compatibility with latest CUDA runtime software. To reduce the overhead of inter-process communications, NVCR efficiently uses SYSV IPC shared memory as CUDA pinned memory. Performance evaluations using micro benchmarks and Amber as a real application show that NVCR’ overhead is acceptably low.</span></p></div>","PeriodicalId":54642,"journal":{"name":"Parallel Computing","volume":"116 ","pages":"Article 103018"},"PeriodicalIF":1.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49728437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Parallel ComputingPub Date : 2023-07-01Epub Date: 2023-02-23DOI: 10.1016/j.parco.2023.103016
Marek Palkowski, Wlodzimierz Bielecki
{"title":"NPDP benchmark suite for the evaluation of the effectiveness of automatic optimizing compilers","authors":"Marek Palkowski, Wlodzimierz Bielecki","doi":"10.1016/j.parco.2023.103016","DOIUrl":"https://doi.org/10.1016/j.parco.2023.103016","url":null,"abstract":"<div><p><span>The paper presents a benchmark suite of ten non-serial polyadic dynamic programming<span> (NPDP) kernels, which are designed to test the efficiency of tiled code generated by polyhedral optimization compilers. These kernels are mainly derived from bioinformatics algorithms, which pose a significant challenge for automatic loop nest tiling transformations. The paper describes algorithms implemented with examined kernels and unifies them in the form of loop nests presented in the C language. The purpose is to reconsider the execution and monitoring of codes, typically used in past and current publications. For carrying out experiments with introduced benchmarks, we applied the two source-to-source compilers, PLuTo and TRACO, to generate cache-efficient codes and analyzed their performance on four multi-core machines. We discuss the limitations of well-known tiling approaches and outline future tiling strategies to generate effective tiled code by means of </span></span>optimizing compilers for introduced benchmarks.</p></div>","PeriodicalId":54642,"journal":{"name":"Parallel Computing","volume":"116 ","pages":"Article 103016"},"PeriodicalIF":1.4,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49728698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}