high performance computing on graphics processing units: hgpu.org

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FDTD on Distributed Heterogeneous Multi-GPU Systems

Eirik Myklebost

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Tags: Algorithms, Benchmarking, Cluster computing, CUDA, Electrodynamics, FDTD, Finite-difference time-domain, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 470, nVidia GeForce GTX 760, Physics, Tesla K20, Thesis, Ultrasound

October 10, 2014 by hgpu

NAS Parallel Benchmarks for GPGPUs using a Directive-based Programming Model

Rengan Xu, Xiaonan Tian, Sunita Chandrasekaran, Yonghong Yan, Barbara Chapman

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Tags: Benchmarking, Computer science, CUDA, MPI, nVidia, OpenACC, Tesla K20

September 28, 2014 by hgpu

Scalability Analysis of Parallel Algorithms on GPU Clusters

Fengshun Lu, Yufei Pang, Fukang Yin

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Tags: Algorithms, Benchmarking, Computer science, CUDA, GPU cluster, nVidia, Tesla M2050

September 25, 2014 by hgpu

Performance analysis of a 240 thread tournament level MCTS Go program on the Intel Xeon Phi

Ali Mirsoleimani, Aske Plaat, Jos Vermaseren, Jaap van den Herik

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Tags: Artificial intelligence, Benchmarking, Computer science, Intel Xeon Phi, Performance

September 16, 2014 by hgpu

Estimating GPU Speedups for Programs Without Writing a Single Line of GPU Code

Newsha Ardalani, Karthikeyan Sankaralingam, Xiaojin Zhu

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 660 Ti, OpenCL, Performance, Tesla K20

August 23, 2014 by hgpu

Practical Symbolic Race Checking of GPU Programs

Peng Li, Guodong Li, Ganesh Gopalakrishnan

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Tags: Benchmarking, Computer science, CUDA, nVidia, Package, Performance

August 19, 2014 by hgpu

Discriminative Convolutional Sum-Product Networks on GPU

Tobias Hartmann

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Tags: Algorithms, Benchmarking, Computer science, Computer vision, CUDA, Neural networks, nVidia, Tesla K20, Thesis

August 1, 2014 by hgpu

A CUDA-Based Real Parameter Optimization Benchmark

Ke Ding, Ying Tan

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 560 Ti, Optimization, Package

July 30, 2014 by hgpu

File I/O on Intel Xeon Phi Coprocessors: RAM disks, VirtIO, NFS and Lustre

Andrey Vladimirov, Vadim Karpusenko, Tony Yoo

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Tags: Benchmarking, Computer science, I/O, Intel Xeon Phi, Portability, Xeon Phi

July 29, 2014 by Andrey Vladimirov

ADHA: Automatic Data layout framework for Heterogeneous Architectures

Deepak Majeti, Kuldeep S. Meel, Rajkishore Barik, Vivek Sarkar

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Tags: Benchmarking, Compilers, Computer science, Heterogeneous systems, nVidia, OpenCL, Programming Languages, Tesla M2050

July 24, 2014 by hgpu

Multi-Core Programming Design Patterns: Stream Processing Algorithms for Dynamic Scene Perceptions

Kannappan Palaniappan

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Tags: Algorithms, Benchmarking, Cell processor, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 460, Tesla C2070

July 24, 2014 by hgpu

On Development, Feasibility, and Limits of Highly Efficient CPU and GPU Programs in Several Fields

David Rohr

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Performance, Review, Thesis

July 14, 2014 by hgpu

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

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

Leveraging the potential of task-based programming with OpenMP task graphs

LLOR: Automatic Repair of OpenMP Programs

LLOR: Automated Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

Unified schemes for directive-based GPU offloading

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

See all packages

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high performance computing on graphics processing units: hgpu.org

FDTD on Distributed Heterogeneous Multi-GPU Systems

NAS Parallel Benchmarks for GPGPUs using a Directive-based Programming Model

Scalability Analysis of Parallel Algorithms on GPU Clusters

Performance analysis of a 240 thread tournament level MCTS Go program on the Intel Xeon Phi

Estimating GPU Speedups for Programs Without Writing a Single Line of GPU Code

Practical Symbolic Race Checking of GPU Programs

Discriminative Convolutional Sum-Product Networks on GPU

A CUDA-Based Real Parameter Optimization Benchmark

File I/O on Intel Xeon Phi Coprocessors: RAM disks, VirtIO, NFS and Lustre

ADHA: Automatic Data layout framework for Heterogeneous Architectures

Multi-Core Programming Design Patterns: Stream Processing Algorithms for Dynamic Scene Perceptions

On Development, Feasibility, and Limits of Highly Efficient CPU and GPU Programs in Several Fields

Recent source codes

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

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

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

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