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hgpu.org » Heterogeneous systems

Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

Joshua Hoke Davis, Christopher Daley, Swaroop Pophale, Thomas Huber, Sunita Chandrasekaran, Nicholas J. Wright

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, OpenMP, Performance, Tesla V100

October 25, 2020 by hgpu

Cross-platform programming model for many-core lattice Boltzmann simulations

Jonas Latt, Christophe Coreixas, Joël Beny

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Tags: Algorithms, cfd, Computational Physics, CUDA, Fluid dynamics, Heterogeneous systems, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 1080, nVidia GeForce RTX 2080, Physics

October 25, 2020 by hgpu

Performance Analysis of Sparse Matrix-Vector Multiplication (SpMV) on Graphics Processing Units (GPUs)

Sarah AlAhmadi, Thaha Mohammed, Aiiad Albeshri, Iyad Katib, Rashid Mehmood

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Tags: Computer science, Heterogeneous systems, HPC, Sparse matrix

October 18, 2020 by hgpu

LoopBench: An Evaluation of Loop Acceleration in Heterogeneous Systems

Saman Biookaghazadeh, Fengbo Ren, Ming Zhao

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Tags: Benchmarking, Computer science, CUDA, FPGA, Heterogeneous systems, OpenCL, Package

October 4, 2020 by hgpu

Adaptation of High Performance and High Capacity Reconfigurable Systems to OpenCL Programming Environments

Davide Russo

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL, Thesis

September 27, 2020 by hgpu

RoadRunner: a fast and flexible exoplanet transit model

Hannu Parviainen

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Tags: Astrophysics, Earth and Planetary Astrophysics, Heterogeneous systems, Instrumentation and Methods for Astrophysics, OpenCL, Package, Python

September 27, 2020 by hgpu

PySchedCL: Leveraging Concurrency in Heterogeneous Data-Parallel Systems

Anirban Ghose, Siddharth Singh, Vivek Kulaharia, Lokesh Dokara, Srijeeta Maity, Soumyajit Dey

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 970, OpenCL, Package

September 20, 2020 by hgpu

Machine Learning in Compilers: Past, Present and Future

Hugh Leather, Chris Cummins

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Tags: Compilers, Computer science, Deep learning, Heterogeneous systems, Machine learning

August 30, 2020 by hgpu

Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads

Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia

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Tags: Computer science, CUDA, Deep learning, FPGA, GPU cluster, Heterogeneous systems, nVidia, Optimization, Task scheduling, Tesla K80, Tesla P100, Tesla V100

August 30, 2020 by hgpu

Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs

Cade Brown, Ahmad Abdelfattah, Stanimire Tomov, Jack Dongarra

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Tags: AMD, AMD Radeon Instinct MI25, AMD Radeon Instinct Mi50, Benchmarking, Computer science, Heterogeneous systems, HIP, HPC, Linear Algebra, Performance, Portability

August 23, 2020 by hgpu

Compiler-Based Tools to Aid in Data Transfer Optimization and On-Chip Debug of Heterogeneous Compute Systems

Matthew B. Ashcraft

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Tags: Compilers, Computer science, FPGA, Heterogeneous systems, HLS, nVidia, nVidia GeForce GTX Titan X, OpenCL, Thesis

August 23, 2020 by hgpu

AIPerf: Automated machine learning as an AI-HPC benchmark

Zhixiang Ren, Yongheng Liu, Tianhui Shi, Lei Xie, Yue Zhou, Jidong Zhai, Youhui Zhang, Yunquan Zhang, Wenguang Chen

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Tags: Algorithms, Artificial intelligence, Benchmarking, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti, Package, Tesla V100

August 23, 2020 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

Cross-platform programming model for many-core lattice Boltzmann simulations

Performance Analysis of Sparse Matrix-Vector Multiplication (SpMV) on Graphics Processing Units (GPUs)

LoopBench: An Evaluation of Loop Acceleration in Heterogeneous Systems

Adaptation of High Performance and High Capacity Reconfigurable Systems to OpenCL Programming Environments

RoadRunner: a fast and flexible exoplanet transit model

PySchedCL: Leveraging Concurrency in Heterogeneous Data-Parallel Systems

Machine Learning in Compilers: Past, Present and Future

Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads

Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs

Compiler-Based Tools to Aid in Data Transfer Optimization and On-Chip Debug of Heterogeneous Compute Systems

AIPerf: Automated machine learning as an AI-HPC benchmark

Recent source codes

tdg-benchs: benchmarks used to test the performance of taskgraph

SW#SYCL

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

HiRep

Gaia AVU-GSR

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

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