high performance computing on graphics processing units: hgpu.org

hgpu.org » Benchmarking

Boosting Java Performance using GPGPUs

James Clarkson, Christos Kotselidis, Gavin Brown, Mikel Lujan

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Java, nVidia, Performance, Tesla K20

August 28, 2015 by hgpu

Automatic Optimization of Thread Mapping for a GPGPU Programming Framework

Kazuhiko Ohno, Tomoharu Kamiya, Takanori Maruyama, Masaki Matsumoto

View

Download (PDF)

Tags: Benchmarking, Compilers, Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 980

July 15, 2015 by hgpu

Evaluating the capabilities of the Xeon Phi platform in the context of software-only, thread-level speculation

Alvaro Estebanez, Diego R. Llanos, Arturo Gonzalez-Escribano

View

Download (PDF)

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

July 13, 2015 by hgpu

Best bang for your buck: GPU nodes for GROMACS biomolecular simulations

Carsten Kutzner, Szilard Pall, Martin Fechner, Ansgar Esztermann, Bert L. de Groot, Helmut Grubmuller

View

Download (PDF)

Source codes

Tags: Benchmarking, Biological Physics, Biomolecules, Computational Physics, Computer science, Molecular dynamics, Molecular simulation, MPI, nVidia, Package, Performance, Tesla K20, Tesla K40

July 6, 2015 by hgpu

Intra-Application Data-Communication Characterization

Imran Ashraf, Vlad-Mihai Sima, Koen Bertels

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GT 640

June 30, 2015 by hgpu

Autotuning Tensor Contraction Computations on GPUs

Axel Rivera, Mary Hall, Paul D. Hovland, Elizabeth Jessup, Thomas Nelson, Boyana Norris

View

Download (PDF)

Tags: Benchmarking, Code generation, Computer science, CUDA, Matrix multiplication, nVidia, OpenACC, Tesla C2050, Tesla K20

June 19, 2015 by hgpu

Practical Symbolic Execution Analysis and Methodology for GPU Programs

Peng Li

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Thesis

June 10, 2015 by hgpu

Machine Learning Based Auto-tuning for Enhanced OpenCL Performance Portability

Thomas L. Falch, Anne C. Elster

View

Download (PDF)

Tags: ATI, ATI Radeon HD 7970, Benchmarking, Computer science, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia, OpenCL, Tesla K40

June 5, 2015 by hgpu

Genetically Improved BarraCUDA

W. B. Langdon, Brian Yee Hong Lam

View

Download (PDF)

Source codes

Tags: Algorithms, Benchmarking, Biology, CUDA, Genomics, nVidia, nVidia GeForce GT 730, Package, Tesla K20, Tesla K40, Tesla K80

June 1, 2015 by hgpu

Particle-in-Cell Laser-Plasma Simulation on Xeon Phi Coprocessors

I.A. Surmin, S.I. Bastrakov, E.S. Efimenko, A.A. Gonoskov, A.V. Korzhimanov, I.B. Meyerov

View

Download (PDF)

Tags: Benchmarking, Computational Physics, Intel Xeon Phi, Particle-in-cell methods, Physics, Plasma physics

May 28, 2015 by hgpu

Key derivation functions and their GPU implementation

Ondrej Mosnacek

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, nVidia, OpenCL, Package, Security, Tesla K20, Thesis

May 22, 2015 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Boosting Java Performance using GPGPUs

Automatic Optimization of Thread Mapping for a GPGPU Programming Framework

Evaluating the capabilities of the Xeon Phi platform in the context of software-only, thread-level speculation

Best bang for your buck: GPU nodes for GROMACS biomolecular simulations

Intra-Application Data-Communication Characterization

Autotuning Tensor Contraction Computations on GPUs

Practical Symbolic Execution Analysis and Methodology for GPU Programs

Machine Learning Based Auto-tuning for Enhanced OpenCL Performance Portability

Genetically Improved BarraCUDA

Particle-in-Cell Laser-Plasma Simulation on Xeon Phi Coprocessors

Key derivation functions and their GPU implementation

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)