The Experts below are selected from a list of 111 Experts worldwide ranked by ideXlab platform

Y Yamaguchi - One of the best experts on this subject based on the ideXlab platform.

  • experience with fine grain communication in em x multiprocessor for parallel Sparse Matrix Computation
    International Parallel Processing Symposium, 1997
    Co-Authors: Mitsuhisa Sato, Yuetsu Kodama, Hirohumi Sakane, Hayato Yamana, Shuichi Sakai, Y Yamaguchi
    Abstract:

    Sparse Matrix problems require a communication paradigm different from those used in conventional distributed-memory multiprocessors. We present in this paper how fine-grain communication can help obtain high performance in the experimental distributed-memory multiprocessor, EM-X, developed at ETL, which can handle fine-grain communication very efficiently. The Sparse Matrix kernel, Conjugate Gradient, is selected for the experiments. Among the steps in CG is the Sparse Matrix vector multiplications we focus on in the study. Some communication methods are developed for performance comparison, including coarse-grain and fine-grain implementations. Fine-grain communication allows exact data access in an unstructured problem to reduce the amount of communication. While CG presents bottlenecks in terms of a large number of fine-grain remote reads, the multithreaded principles of execution is so designed to tolerate such latency. Experimental results indicate that the performance of fine-grain read implementation is comparable to that of coarse-grain implementation on 64 processors. The results demonstrate that fine-grain communication can be a viable and efficient approach to unstructured Sparse Matrix problems on large-scale distributed-memory multiprocessors.

  • IPPS - Experience with fine-grain communication in EM-X multiprocessor for parallel Sparse Matrix Computation
    Proceedings 11th International Parallel Processing Symposium, 1
    Co-Authors: Mitsuhisa Sato, Yuetsu Kodama, Hirohumi Sakane, Hayato Yamana, Shuichi Sakai, Y Yamaguchi
    Abstract:

    Sparse Matrix problems require a communication paradigm different from those used in conventional distributed-memory multiprocessors. We present in this paper how fine-grain communication can help obtain high performance in the experimental distributed-memory multiprocessor, EM-X, developed at ETL, which can handle fine-grain communication very efficiently. The Sparse Matrix kernel, Conjugate Gradient, is selected for the experiments. Among the steps in CG is the Sparse Matrix vector multiplications we focus on in the study. Some communication methods are developed for performance comparison, including coarse-grain and fine-grain implementations. Fine-grain communication allows exact data access in an unstructured problem to reduce the amount of communication. While CG presents bottlenecks in terms of a large number of fine-grain remote reads, the multithreaded principles of execution is so designed to tolerate such latency. Experimental results indicate that the performance of fine-grain read implementation is comparable to that of coarse-grain implementation on 64 processors. The results demonstrate that fine-grain communication can be a viable and efficient approach to unstructured Sparse Matrix problems on large-scale distributed-memory multiprocessors.

Mitsuhisa Sato - One of the best experts on this subject based on the ideXlab platform.

  • experience with fine grain communication in em x multiprocessor for parallel Sparse Matrix Computation
    International Parallel Processing Symposium, 1997
    Co-Authors: Mitsuhisa Sato, Yuetsu Kodama, Hirohumi Sakane, Hayato Yamana, Shuichi Sakai, Y Yamaguchi
    Abstract:

    Sparse Matrix problems require a communication paradigm different from those used in conventional distributed-memory multiprocessors. We present in this paper how fine-grain communication can help obtain high performance in the experimental distributed-memory multiprocessor, EM-X, developed at ETL, which can handle fine-grain communication very efficiently. The Sparse Matrix kernel, Conjugate Gradient, is selected for the experiments. Among the steps in CG is the Sparse Matrix vector multiplications we focus on in the study. Some communication methods are developed for performance comparison, including coarse-grain and fine-grain implementations. Fine-grain communication allows exact data access in an unstructured problem to reduce the amount of communication. While CG presents bottlenecks in terms of a large number of fine-grain remote reads, the multithreaded principles of execution is so designed to tolerate such latency. Experimental results indicate that the performance of fine-grain read implementation is comparable to that of coarse-grain implementation on 64 processors. The results demonstrate that fine-grain communication can be a viable and efficient approach to unstructured Sparse Matrix problems on large-scale distributed-memory multiprocessors.

  • IPPS - Experience with fine-grain communication in EM-X multiprocessor for parallel Sparse Matrix Computation
    Proceedings 11th International Parallel Processing Symposium, 1
    Co-Authors: Mitsuhisa Sato, Yuetsu Kodama, Hirohumi Sakane, Hayato Yamana, Shuichi Sakai, Y Yamaguchi
    Abstract:

    Sparse Matrix problems require a communication paradigm different from those used in conventional distributed-memory multiprocessors. We present in this paper how fine-grain communication can help obtain high performance in the experimental distributed-memory multiprocessor, EM-X, developed at ETL, which can handle fine-grain communication very efficiently. The Sparse Matrix kernel, Conjugate Gradient, is selected for the experiments. Among the steps in CG is the Sparse Matrix vector multiplications we focus on in the study. Some communication methods are developed for performance comparison, including coarse-grain and fine-grain implementations. Fine-grain communication allows exact data access in an unstructured problem to reduce the amount of communication. While CG presents bottlenecks in terms of a large number of fine-grain remote reads, the multithreaded principles of execution is so designed to tolerate such latency. Experimental results indicate that the performance of fine-grain read implementation is comparable to that of coarse-grain implementation on 64 processors. The results demonstrate that fine-grain communication can be a viable and efficient approach to unstructured Sparse Matrix problems on large-scale distributed-memory multiprocessors.

Steve Reinhardt - One of the best experts on this subject based on the ideXlab platform.

  • graph coloring on the gpu and some techniques to improve load imbalance
    International Parallel and Distributed Processing Symposium, 2015
    Co-Authors: Shuai Che, Gregory Rodgers, Brad Beckmann, Steve Reinhardt
    Abstract:

    Graphics processing units (GPUs) have been increasingly used to accelerate irregular applications such as graph and Sparse-Matrix Computation. Graph coloring is a key building block for many graph applications. The first step of many graph applications is graph coloring/partitioning to obtain sets of independent vertices for subsequent parallel Computations. However, parallelization and optimization of coloring for GPUs have been a challenge for programmers. This paper studies approaches to implementing graph coloring on a GPU and characterizes their program behaviors with different graph structures. We also investigate load imbalance, which can be the main cause for performance bottlenecks. We evaluate the effectiveness of different optimization techniques, including the use of work stealing and the design of a hybrid algorithm. We are able to improve graph coloring performance by approximately 25% compared to a baseline GPU implementation on an AMD Radeon HD 7950 GPU. We also analyze some important factors affecting performance.

  • IPDPS Workshops - Graph Coloring on the GPU and Some Techniques to Improve Load Imbalance
    2015 IEEE International Parallel and Distributed Processing Symposium Workshop, 2015
    Co-Authors: Shuai Che, Gregory Rodgers, Brad Beckmann, Steve Reinhardt
    Abstract:

    Graphics processing units (GPUs) have been increasingly used to accelerate irregular applications such as graph and Sparse-Matrix Computation. Graph coloring is a key building block for many graph applications. The first step of many graph applications is graph coloring/partitioning to obtain sets of independent vertices for subsequent parallel Computations. However, parallelization and optimization of coloring for GPUs have been a challenge for programmers. This paper studies approaches to implementing graph coloring on a GPU and characterizes their program behaviors with different graph structures. We also investigate load imbalance, which can be the main cause for performance bottlenecks. We evaluate the effectiveness of different optimization techniques, including the use of work stealing and the design of a hybrid algorithm. We are able to improve graph coloring performance by approximately 25% compared to a baseline GPU implementation on an AMD Radeon HD 7950 GPU. We also analyze some important factors affecting performance.

Hayato Yamana - One of the best experts on this subject based on the ideXlab platform.

  • experience with fine grain communication in em x multiprocessor for parallel Sparse Matrix Computation
    International Parallel Processing Symposium, 1997
    Co-Authors: Mitsuhisa Sato, Yuetsu Kodama, Hirohumi Sakane, Hayato Yamana, Shuichi Sakai, Y Yamaguchi
    Abstract:

    Sparse Matrix problems require a communication paradigm different from those used in conventional distributed-memory multiprocessors. We present in this paper how fine-grain communication can help obtain high performance in the experimental distributed-memory multiprocessor, EM-X, developed at ETL, which can handle fine-grain communication very efficiently. The Sparse Matrix kernel, Conjugate Gradient, is selected for the experiments. Among the steps in CG is the Sparse Matrix vector multiplications we focus on in the study. Some communication methods are developed for performance comparison, including coarse-grain and fine-grain implementations. Fine-grain communication allows exact data access in an unstructured problem to reduce the amount of communication. While CG presents bottlenecks in terms of a large number of fine-grain remote reads, the multithreaded principles of execution is so designed to tolerate such latency. Experimental results indicate that the performance of fine-grain read implementation is comparable to that of coarse-grain implementation on 64 processors. The results demonstrate that fine-grain communication can be a viable and efficient approach to unstructured Sparse Matrix problems on large-scale distributed-memory multiprocessors.

  • IPPS - Experience with fine-grain communication in EM-X multiprocessor for parallel Sparse Matrix Computation
    Proceedings 11th International Parallel Processing Symposium, 1
    Co-Authors: Mitsuhisa Sato, Yuetsu Kodama, Hirohumi Sakane, Hayato Yamana, Shuichi Sakai, Y Yamaguchi
    Abstract:

    Sparse Matrix problems require a communication paradigm different from those used in conventional distributed-memory multiprocessors. We present in this paper how fine-grain communication can help obtain high performance in the experimental distributed-memory multiprocessor, EM-X, developed at ETL, which can handle fine-grain communication very efficiently. The Sparse Matrix kernel, Conjugate Gradient, is selected for the experiments. Among the steps in CG is the Sparse Matrix vector multiplications we focus on in the study. Some communication methods are developed for performance comparison, including coarse-grain and fine-grain implementations. Fine-grain communication allows exact data access in an unstructured problem to reduce the amount of communication. While CG presents bottlenecks in terms of a large number of fine-grain remote reads, the multithreaded principles of execution is so designed to tolerate such latency. Experimental results indicate that the performance of fine-grain read implementation is comparable to that of coarse-grain implementation on 64 processors. The results demonstrate that fine-grain communication can be a viable and efficient approach to unstructured Sparse Matrix problems on large-scale distributed-memory multiprocessors.

Shuichi Sakai - One of the best experts on this subject based on the ideXlab platform.

  • experience with fine grain communication in em x multiprocessor for parallel Sparse Matrix Computation
    International Parallel Processing Symposium, 1997
    Co-Authors: Mitsuhisa Sato, Yuetsu Kodama, Hirohumi Sakane, Hayato Yamana, Shuichi Sakai, Y Yamaguchi
    Abstract:

    Sparse Matrix problems require a communication paradigm different from those used in conventional distributed-memory multiprocessors. We present in this paper how fine-grain communication can help obtain high performance in the experimental distributed-memory multiprocessor, EM-X, developed at ETL, which can handle fine-grain communication very efficiently. The Sparse Matrix kernel, Conjugate Gradient, is selected for the experiments. Among the steps in CG is the Sparse Matrix vector multiplications we focus on in the study. Some communication methods are developed for performance comparison, including coarse-grain and fine-grain implementations. Fine-grain communication allows exact data access in an unstructured problem to reduce the amount of communication. While CG presents bottlenecks in terms of a large number of fine-grain remote reads, the multithreaded principles of execution is so designed to tolerate such latency. Experimental results indicate that the performance of fine-grain read implementation is comparable to that of coarse-grain implementation on 64 processors. The results demonstrate that fine-grain communication can be a viable and efficient approach to unstructured Sparse Matrix problems on large-scale distributed-memory multiprocessors.

  • IPPS - Experience with fine-grain communication in EM-X multiprocessor for parallel Sparse Matrix Computation
    Proceedings 11th International Parallel Processing Symposium, 1
    Co-Authors: Mitsuhisa Sato, Yuetsu Kodama, Hirohumi Sakane, Hayato Yamana, Shuichi Sakai, Y Yamaguchi
    Abstract:

    Sparse Matrix problems require a communication paradigm different from those used in conventional distributed-memory multiprocessors. We present in this paper how fine-grain communication can help obtain high performance in the experimental distributed-memory multiprocessor, EM-X, developed at ETL, which can handle fine-grain communication very efficiently. The Sparse Matrix kernel, Conjugate Gradient, is selected for the experiments. Among the steps in CG is the Sparse Matrix vector multiplications we focus on in the study. Some communication methods are developed for performance comparison, including coarse-grain and fine-grain implementations. Fine-grain communication allows exact data access in an unstructured problem to reduce the amount of communication. While CG presents bottlenecks in terms of a large number of fine-grain remote reads, the multithreaded principles of execution is so designed to tolerate such latency. Experimental results indicate that the performance of fine-grain read implementation is comparable to that of coarse-grain implementation on 64 processors. The results demonstrate that fine-grain communication can be a viable and efficient approach to unstructured Sparse Matrix problems on large-scale distributed-memory multiprocessors.