high performance computing on graphics processing units: hgpu.org

David Abdurachmanov, Kapil Arya, Josh Bendavid, Tommaso Boccali, Gene Cooperman, Andrea Dotti, Peter Elmer, Giulio Eulisse, Francesco Giacomini, Christopher D. Jones, Matteo Manzali, Shahzad Muzaffar

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Tags: ARM, Computational Physics, High Energy Physics - Experiment, Intel Phi, Intel Xeon Phi, Performance, Physics

November 11, 2013 by hgpu

Performance Analysis of Sobel Edge Detection Filter on GPU using CUDA & OpenGL

Khyati Shah

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GT 130, OpenGL, Performance

November 10, 2013 by hgpu

Dynamic autotuning of adaptive fast multipole methods on hybrid multicore CPU & GPU systems

Marcus Holm, Stefan Engblom, Anders Goude, Sverker Holmgren

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Tags: Computer science, CUDA, Fast multipole method, nVidia, Package, Performance, Tesla M2050

November 6, 2013 by hgpu

Comparative Performance Analysis of Intel Xeon Phi, GPU, and CPU

George Teodoro, Tahsin Kurc, Jun Kong, Lee Cooper, Joel Saltz

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Tags: Computer science, CUDA, Intel Phi, Intel Xeon Phi, Microscopy, nVidia, OpenMP, Performance, Task scheduling, Tesla K20

November 6, 2013 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

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high performance computing on graphics processing units: hgpu.org

Exploring Graphics Processing Unit (GPU) Resource Sharing Efficiency for High Performance Computing

Dynamic Partitioning-based JPEG Decompression on Heterogeneous Multicore Architectures

Multi-GPU Support on the Marrow Algorithmic Skeleton Framework

Adaptive implementation selection in the SkePU skeleton programming library

Performance and Power Comparisons Between Fermi and Cypress GPUs

SAGE: Self-Tuning Approximation for Graphics Engines

Anatomy of High-Performance Many-Threaded Matrix Multiplication

Performance Evaluation of R with Intel Xeon Phi Coprocessor

Explorations of the Viability of ARM and Xeon Phi for Physics Processing

Performance Analysis of Sobel Edge Detection Filter on GPU using CUDA & OpenGL

Dynamic autotuning of adaptive fast multipole methods on hybrid multicore CPU & GPU systems

Comparative Performance Analysis of Intel Xeon Phi, GPU, and CPU

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)