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HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

Masab Ahmad, Halit Dogan, Christopher J. Michael, Omer Khan

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Tags: Algorithms, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 970, OpenCL, Package, Performance

March 31, 2019 by hgpu

swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight

Jiarui Fang, Liandeng Li, Haohuan Fu, Jinlei Jiang, Wenlai Zhao, Conghui He, Xin You, Guangwen Yang

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, OpenMPI, Package, Tesla K40

March 24, 2019 by hgpu

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Zhe Jia, Marco Maggioni, Jeffrey Smith, Daniele Paolo Scarpazza

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Tags: Benchmarking, Computer science, CUDA, Deep learning, nVidia, Performance, PTX, Tesla P100, Tesla V100

March 24, 2019 by hgpu

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

Hamidreza Khaleghzadeh

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Matrix multiplication, nVidia, OpenCL, Package, Thesis

March 17, 2019 by hgpu

Performance Optimization of Memory Intensive Applications on FPGA Accelerator

Arslan Arif

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Tags: Artificial intelligence, Computer science, Electronic design automation, FPGA, nVidia, nVidia GeForce GTX 960, OpenCL, Thesis

March 17, 2019 by hgpu

Analyzing GPU Tensor Core Potential for Fast Reductions

Roberto Carrasco, Raimundo Vega, Cristóbal A. Navarro

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Tags: Computer science, CUDA, Deep learning, nVidia, Performance, Tesla V100

March 17, 2019 by hgpu

TensorFlow Doing HPC

Steven W. D. Chien, Stefano Markidis, Vyacheslav Olshevsky, Yaroslav Bulatov, Erwin Laure, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, Deep learning, FFT, Heterogeneous systems, HPC, Machine learning, nVidia, nVidia Quadro K420, OpenMPI, Package, Performance, Python, TensorFlow, Tesla K80, Tesla V100

March 17, 2019 by hgpu

On the Portability of GPU-Accelerated Applications via Automated Source-to-Source Translation

Paul Sathre, Mark Gardner, Wu-chun Feng

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Tags: AMD FirePro S9150, ATI, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, OpenCL, Package, performance portability, Tesla K80

March 10, 2019 by hgpu

Custom Code Generation for a Graph DSL

Bikash Gogoi, Unnikrishnan Cheramangalath, Rupesh Nasre

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Tags: Code generation, Computer science, CUDA, DSL, nVidia, Tesla K40

March 10, 2019 by hgpu

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang

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Tags: Computer science, Graph theory, Machine learning, nVidia, nVidia GeForce GTX 1080, Tesla P100

March 10, 2019 by hgpu

Using Compiler Directives for Performance Portability in Scientific Computing: Kernels from Molecular Simulation

Ada Sedova, Andreas Tillack, Arnold Tharrington

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Tags: Chemistry, Molecular dynamics, Molecular simulation, nVidia, OpenACC, performance portability, Physics, Tesla K20

March 3, 2019 by hgpu

Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL

Maria Angelica Davila Guzman, Raul Nozal, Ruben Gran Tejero, Maria Villarroya-Gaudo, Dario Suarez Gracia, Jose Luis Bosque

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Tags: Computer science, FPGA, Heterogeneous systems, load balancing, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

March 3, 2019 by hgpu

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

Engineering Supercomputing Platforms for Biomolecular Applications

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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high performance computing on graphics processing units: hgpu.org

HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

Performance Optimization of Memory Intensive Applications on FPGA Accelerator

Analyzing GPU Tensor Core Potential for Fast Reductions

TensorFlow Doing HPC

On the Portability of GPU-Accelerated Applications via Automated Source-to-Source Translation

Custom Code Generation for a Graph DSL

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Using Compiler Directives for Performance Portability in Scientific Computing: Kernels from Molecular Simulation

Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL

Recent source codes

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

microSYCL: SYCL micro-benchmarks repository

XaaS containers

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

SYCL Container

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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