high performance computing on graphics processing units: hgpu.org

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Optimization principles and application performance evaluation of a multithreaded GPU using CUDA

Shane Ryoo, Christopher I. Rodrigues, Sara S. Baghsorkhi, Sam S. Stone, David B. Kirk, Wen-mei W. Hwu

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce 8800 GTX, Performance, Programming techniques

October 27, 2010 by hgpu

Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

Victor W. Lee,Changkyu Kim,Jatin Chhugani,Michael Deisher,Daehyun Kim,Anthony D. Nguyen,Nadathur Satish,Mikhail Smelyanskiy,Srinivas Chennupaty,Per Hammarlund,Ronak Singhal,Pradeep Dubey

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Tags: Algorithm optimization, Computer science, nVidia, nVidia GeForce GTX 280, Performance

October 27, 2010 by hgpu

On the limits of GPU acceleration

Richard Vuduc, Aparna Chandramowlishwaran, Jee Choi, Murat Guney, Aashay Shringarpure

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Tags: Computer science, nVidia, nVidia GeForce GTX 285, Performance, Tesla C1060, Tesla S1070

October 27, 2010 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

Optimization principles and application performance evaluation of a multithreaded GPU using CUDA

Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

On the limits of GPU acceleration

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)