Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the explosion of big data analytics, scalinglinear algebra packages has become extremely important. Inthe context of GPUs, cuBLAS API provides a highly efficientpackage for linear algebra subroutines on a single GPU. Dueto inputs of large dimensions, it often becomes necessary tocompute over clusters. Hover, the package does not providefacilities for computing over a 'cluster of GPUs' efficiently. Inthis paper,  demonstrate a high level framework for scalinglinear algebra computations across a cluster of GPUs, throughmatrix multiplication problem. In particular,  describe amethod of specifying matrices using porlists that capturesboth parallelism and recursion succinctly, and automaticallyschedule partitioned matrices over a GPU cluster to gainthe advantages of cuBLAS for computing the product ofpartitioned matrices over a cluster of GPUs. Our experimentalresults show significant performance gains, of the order ofat least 132% for large matrices over that of a single GPUcomputation. The method reflects the map-reduce paradigmwhere the matrices are mapped to appropriate partitionedmatrices and sent to appropriate members of the clusters andthe results are collected to obtain the resultant matrix.
['Anshu S. Anand', 'R. K. Shyamasundar']
I am writing a research paper on a idea Scaling Computation on GPUs Using Powerlists
[{"from": "human", "value": "I am writing a research paper on a idea Scaling Computation on GPUs Using Powerlists"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the explosion of big data analytics, scalinglinear algebra packages has become extremely important. Inthe context of GPUs, cuBLAS API provides a highly efficientpackage for linear algebra subroutines on a single GPU. Dueto inputs of large dimensions, it often becomes necessary tocompute over clusters. Hover, the package does not providefacilities for computing over a 'cluster of GPUs' efficiently. Inthis paper,  demonstrate a high level framework for scalinglinear algebra computations across a cluster of GPUs, throughmatrix multiplication problem. In particular,  describe amethod of specifying matrices using porlists that capturesboth parallelism and recursion succinctly, and automaticallyschedule partitioned matrices over a GPU cluster to gainthe advantages of cuBLAS for computing the product ofpartitioned matrices over a cluster of GPUs. Our experimentalresults show significant performance gains, of the order ofat least 132% for large matrices over that of a single GPUcomputation. The method reflects the map-reduce paradigmwhere the matrices are mapped to appropriate partitionedmatrices and sent to appropriate members of the clusters andthe results are collected to obtain the resultant matrix."}]