Jaehyung Yang, Ashfaq A. Khokhar, Sohail Sheikht, Arif Ghafoor
{"title":"Estimating execution time for parallel tasks in heterogeneous processing (HP) environment","authors":"Jaehyung Yang, Ashfaq A. Khokhar, Sohail Sheikht, Arif Ghafoor","doi":"10.1109/HCW.1994.324966","DOIUrl":"https://doi.org/10.1109/HCW.1994.324966","url":null,"abstract":"Mapping of application program tasks onto a suite of heterogeneous machines requires the estimation of execution times of the tasks on these machines. In this paper, an efficient methodology for estimating the execution times of a given program on various machines available in an HP environment is presented. The methodology uses parametric code profiling and parametric analytical benchmarking techniques and incorporates the concept of an architecture-independent computation model to estimate the execution times.<<ETX>>","PeriodicalId":327341,"journal":{"name":"Proceedings Heterogeneous Computing Workshop","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125347902","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":"Heterogeneous computing for vision","authors":"R. Nevatia","doi":"10.1109/HCW.1994.324964","DOIUrl":"https://doi.org/10.1109/HCW.1994.324964","url":null,"abstract":"Identifies the computational requirements of tasks in vision. Vision tasks usually require three levels of processing/spl minus/low, intermediate and high/spl minus/each having distinctly different characteristics thus pointing to the need for heterogeneous computing. An example from the domain of photo-interpretation is used to illustrate some of the specifics of the processing levels. Finally, we discuss the pros and cons of using special-purpose and commercially available parallel computers for vision tasks.<<ETX>>","PeriodicalId":327341,"journal":{"name":"Proceedings Heterogeneous Computing Workshop","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123455549","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":"Dynamic optimization of load distribution in heterogeneous systems","authors":"E. Haddad","doi":"10.1109/HCW.1994.324965","DOIUrl":"https://doi.org/10.1109/HCW.1994.324965","url":null,"abstract":"Considers at run-time t the dynamic load redistribution of the m(t) interacting heterogeneous unexecuted modules of a given workload over the p(t) heterogeneous processors available at time t. Average-valued parameters are used to model the workload parameters and its execution on the system processors. We seek to minimize a multi-metric objective function representing a combination of workload completion time, communication cost, resource utilization cost, and processor idle-time cost. The efficacy of a processor is defined as a composite measure of its CPU speed, memory speed, and the averaged workload parameters. The optimal load distribution is found to be the apportionment of the total load among a subset of the available processors, composed of the q(t)/spl les/p(t) most efficacious processors, in direct proportion to their efficacies.<<ETX>>","PeriodicalId":327341,"journal":{"name":"Proceedings Heterogeneous Computing Workshop","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115307350","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}
Song Chen, M. M. Eshaghian, R. F. Freund, J. Potter, Ying-Chieh Wu
{"title":"Scalable heterogeneous programming tools","authors":"Song Chen, M. M. Eshaghian, R. F. Freund, J. Potter, Ying-Chieh Wu","doi":"10.1109/HCW.1994.324957","DOIUrl":"https://doi.org/10.1109/HCW.1994.324957","url":null,"abstract":"We study the heterogeneous use of two programming paradigms for heterogeneous computing called Cluster-M and HAsC. Both paradigms can efficiently support heterogeneous networks by preserving a level of abstraction which does not include any architecture mapping details. Furthermore, they are both machine independent and hence are scalable. Unlike almost all existing heterogeneous orchestration tools which are MIMD based, HAsC is based on the fundamental concepts of SIMD associative computing. HAsC models a heterogeneous network as a coarse grained associative computer and is designed to optimize the execution of problems with large ratios of computations to instructions. Ease of programming and execution speed, not the utilization of idle resources are the primary goals of HAsC. On the other hand, Cluster-M is a generic technique that can be applied to both coarse grained as well as fine grained networks. Cluster-M provides an environment for porting various tasks onto the machines in a heterogeneous suite such that resource utilization is maximized and the overall execution time is minimized. We illustrate how these two paradigms can be used together to provide an efficient medium for heterogeneous programming. Finally, their scalability is discussed.<<ETX>>","PeriodicalId":327341,"journal":{"name":"Proceedings Heterogeneous Computing Workshop","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114345030","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":"A generic multi virtual machines architecture for distributed parallel operating systems design","authors":"Traian Muntean","doi":"10.1109/HCW.1994.324955","DOIUrl":"https://doi.org/10.1109/HCW.1994.324955","url":null,"abstract":"The author presents the architecture of a new family of parallel operating systems based on an alternative design approach when compared to multiprogramming, namely, generic construction of parallel virtual machines. This construction is made according to needs of models for parallel programming which shall be supported as subsystems by the parallel operating system kernel. The author presents the PAROS architecture and its generic kernel ParX which has been designed for message passing parallel computers. Several parallel virtual machines are also described, such as for instance a diffusion virtual machine, and a message passing-shared data virtual machine.<<ETX>>","PeriodicalId":327341,"journal":{"name":"Proceedings Heterogeneous Computing Workshop","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114760618","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}
J. C. DeSouza-Batista, M. M. Eshaghian, A. C. Parker, S. Prakash, Y.C. Wu
{"title":"A sub-optimal assignment of application tasks onto heterogeneous systems","authors":"J. C. DeSouza-Batista, M. M. Eshaghian, A. C. Parker, S. Prakash, Y.C. Wu","doi":"10.1109/HCW.1994.324968","DOIUrl":"https://doi.org/10.1109/HCW.1994.324968","url":null,"abstract":"Presents a mathematical programming model for finding the sub-optimal assignment of a given application task onto a heterogeneous suite of computers. The proposed model is based on the Cluster-M heterogeneous programming paradigm. Using Cluster-M, an application task is represented in form of a Cluster-M specification which indicates all the concurrent and communicating subtasks at every step of computation. These specifications are then to be mapped onto the underlying heterogeneous suite of computers represented in a Cluster-M representation format. To map every step of Cluster-M specification onto the Cluster-M representation of the heterogeneous suite, we propose to use an optimal synthesis technique called SOS (synthesis of systems). We formulate and solve each of the mapping steps in the form of a mixed integer linear programming model. This leads to a very fast sub-optimal selection and mapping solution.<<ETX>>","PeriodicalId":327341,"journal":{"name":"Proceedings Heterogeneous Computing Workshop","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116214877","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}