high performance computing on graphics processing units: hgpu.org

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SCALSALE: Scalable SALE Benchmark Framework for Supercomputers

Re'em Harel, Matan Rusanovsky, Ron Wagner, Harel Levin, Gal Oren

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Tags: Benchmarking, Computer science, Fortran, HPC, MPI, Package, Performance

September 11, 2022 by hgpu

Demystifying the Nvidia Ampere Architecture through Microbenchmarking and Instruction-level Analysis

Hamdy Abdelkhalik, Yehia Arafa, Nandakishore Santhi, Abdel-Hameed Badawy

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Tags: Benchmarking, Computer science, Hardware Architecture, Latency, nVidia, nVidia A100, Performance, PTX

August 28, 2022 by hgpu

Exploring Thread Coarsening on FPGA

Mostafa Eghbali Zarch, Reece Neff, Michela Becchi

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Tags: Compilers, Computer science, FPGA, OpenCL, Performance, performance portability

August 28, 2022 by hgpu

Performance analysis of matrix-free conjugate gradient kernels using SYCL

Igor Baratta, Chris Richardson, Garth Wells

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Tags: Benchmarking, Computer science, Latency, nVidia, nVidia A100, OpenCL, Package, Performance, SYCL

August 21, 2022 by hgpu

Reducing Synchronous GPU Memory Transfers: Design and implementation of a Futhark compiler optimisation

Philip Jon Børgesen

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, CUDA, nVidia, nVidia A100, OpenCL, Performance, Thesis

July 17, 2022 by hgpu

High Performance Simulation for Scalable Multi-Agent Reinforcement Learning

Jordan Langham-Lopez, Sebastian M. Schmon, Patrick Cannon

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia GeForce GTX 1650, Performance

July 17, 2022 by hgpu

Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

Germán Castaño, Youssef Faqir-Rhazoui, Carlos García, Manuel Prieto-Matías

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Package, Performance, SYCL

July 3, 2022 by hgpu

Deep Learning Models on CPUs: A Methodology for Efficient Training

Quchen Fu, Ramesh Chukka, Keith Achorn, Thomas Atta-fosu, Deepak R. Canchi, Zhongwei Teng, Jules White, Douglas C. Schmidt

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Tags: Computer science, Deep learning, Performance, PyTorch, Visualization

June 26, 2022 by hgpu

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

Fucheng Jia, Deyu Zhang, Ting Cao, Shiqi Jiang, Yunxin Liu, Ju Ren, Yaoxue Zhang

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Tags: Computer science, Deep learning, Heterogeneous systems, Package, Performance

June 19, 2022 by hgpu

Modeling GPU Dynamic Parallelism for Self Similar Density Workloads

Felipe A. Quezada, Cristóbal A. Navarro, Miguel Romero, Cristhian Aguilera

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce Titan V, nVidia Jetson NX, nVidia Titan RTX, Performance

June 12, 2022 by hgpu

On the Compilation Performance of Current SYCL Implementations

Peter Thoman, Facundo Molina Heredia, Thomas Fahringer

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Tags: Code generation, Compilers, Computer science, Heterogeneous systems, OpenCL, Package, Performance, SYCL

June 12, 2022 by hgpu

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors

Wei Sun, Ang Li, Tong Geng, Sander Stuijk, Henk Corporaal

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Tags: Benchmarking, Computer science, Matrix multiplication, nVidia, nVidia A100, nVidia GeForce RTX 2080, nVidia GeForce RTX 2080 Ti, Performance, PTX, Sparse matrix

June 12, 2022 by hgpu

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high performance computing on graphics processing units: hgpu.org

SCALSALE: Scalable SALE Benchmark Framework for Supercomputers

Demystifying the Nvidia Ampere Architecture through Microbenchmarking and Instruction-level Analysis

Exploring Thread Coarsening on FPGA

Performance analysis of matrix-free conjugate gradient kernels using SYCL

Reducing Synchronous GPU Memory Transfers: Design and implementation of a Futhark compiler optimisation

High Performance Simulation for Scalable Multi-Agent Reinforcement Learning

Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

Deep Learning Models on CPUs: A Methodology for Efficient Training

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

Modeling GPU Dynamic Parallelism for Self Similar Density Workloads

On the Compilation Performance of Current SYCL Implementations

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors

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