Satoshi Matsuoka

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Association for Computing Machinery

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Matrix Engines for High Performance Computing:A Paragon of Performance or Grasping at Straws?

Matrix engines or units, in different forms and affinities, are becoming...
10/27/2020 ∙ by Jens Domke, et al. ∙ 0 ∙ share

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Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA

The dedicated memory of hardware accelerators can be insufficient to sto...
08/26/2020 ∙ by Mohamed Wahib, et al. ∙ 0 ∙ share

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The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism

We present scalable hybrid-parallel algorithms for training large-scale ...
07/25/2020 ∙ by Yosuke Oyama, et al. ∙ 0 ∙ share

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A Study of Single and Multi-device Synchronization Methods in Nvidia GPUs

GPUs are playing an increasingly important role in general-purpose compu...
04/11/2020 ∙ by Lingqi Zhang, et al. ∙ 0 ∙ share

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A Survey on Coarse-Grained Reconfigurable Architectures from a Performance Perspective

With the end of both Dennard's scaling and Moore's law, computer users a...
04/09/2020 ∙ by Artur Podobas, et al. ∙ 0 ∙ share

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High-Performance High-Order Stencil Computation on FPGAs Using OpenCL

In this paper we evaluate the performance of FPGAs for high-order stenci...
02/14/2020 ∙ by Hamid Reza Zohouri, et al. ∙ 0 ∙ share

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AN5D: Automated Stencil Framework for High-Degree Temporal Blocking on GPUs

Stencil computation is one of the most widely-used compute patterns in h...
01/06/2020 ∙ by Kazuaki Matsumura, et al. ∙ 0 ∙ share

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The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Supported by their high power efficiency and recent advancements in High...
10/15/2019 ∙ by Hamid Reza Zohouri, et al. ∙ 0 ∙ share

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iFDK: A Scalable Framework for Instant High-resolution Image Reconstruction

Computed Tomography (CT) is a widely used technology that requires compu...
09/06/2019 ∙ by Peng Chen, et al. ∙ 0 ∙ share

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A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels

This paper proposes a versatile high-performance execution model, inspir...
07/14/2019 ∙ by Peng Chen, et al. ∙ 0 ∙ share

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Batched Sparse Matrix Multiplication for Accelerating Graph Convolutional Networks

Graph Convolutional Networks (GCNs) are recently getting much attention ...
03/27/2019 ∙ by Yusuke Nagasaka, et al. ∙ 0 ∙ share

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A New Linear Time Correctness Condition for Multiplicative Linear Logic

In this paper, we give a new linear time correctness condition for proof...
02/26/2019 ∙ by Satoshi Matsuoka, et al. ∙ 0 ∙ share

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Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs

Graph pattern matching algorithms to handle million-scale dynamic graphs...
12/21/2018 ∙ by Hiroki Kanezashi, et al. ∙ 0 ∙ share

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Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs

Large-scale distributed training of deep neural networks suffer from the...
11/29/2018 ∙ by Kazuki Osawa, et al. ∙ 0 ∙ share

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Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?

Among the (uncontended) common wisdom in High-Performance Computing (HPC...
10/22/2018 ∙ by Jens Domke, et al. ∙ 0 ∙ share

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μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching

NVIDIA cuDNN is a low-level library that provides GPU kernels frequently...
04/13/2018 ∙ by Yosuke Oyama, et al. ∙ 0 ∙ share

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High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

Sparse matrix-matrix multiplication (SpGEMM) is a computational primitiv...
04/05/2018 ∙ by Yusuke Nagasaka, et al. ∙ 0 ∙ share

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Combined Spatial and Temporal Blocking for High-Performance Stencil Computation on FPGAs Using OpenCL

Recent developments in High Level Synthesis tools have attracted softwar...
02/01/2018 ∙ by Hamid Reza Zohouri, et al. ∙ 0 ∙ share

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