high performance computing on graphics processing units: hgpu.org

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Parallel programming with inductive synthesis

Shaon Barman, Rastislav Bodik, Sagar Jain, Yewen Pu, Saurabh Srivastava, Nicholas Tung

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Performance, Programming techniques

December 19, 2011 by hgpu

Massively Parallel Logic Simulation with GPUs

Yuhao Zhu, Bo Wang, Yangdong Deng

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Tags: Algorithms, Computer science, CUDA, Electronic design automation, nVidia, nVidia GeForce GTX 280, Optimization, Performance

December 17, 2011 by hgpu

Customizable Memory Schemes for Data Parallel Accelerators

Chunyang Gou

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques, Thesis

December 17, 2011 by hgpu

A Comparison of Modern GPU and CPU Architectures: And the Common Convergence of Both

Jonathan Palacios, Josh Triska

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Tags: Computer science, Hardware Architecture, nVidia, Overview, Performance

December 17, 2011 by hgpu

Improving GPU Robustness by Making Use of Faulty Parts

Artem Durytskyy, Mohamed Zahran, Ramesh Karri

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Tags: Computer science, Fault simulation, Hardware Architecture, nVidia, Performance

December 16, 2011 by hgpu

Simultaneous Branch and Warp Interweaving for Sustained GPU Performance

Nicolas Brunie, Sylvain Collange, Gregory Diamos

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Tags: Computer science, CUDA, nVidia, Performance

December 15, 2011 by hgpu

Performance in GPU Architectures: Potentials and Distances

Ahmad Lashgar, Amirali Baniasadi

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro FX 5800, Performance

December 15, 2011 by hgpu

Graph Generation on GPUs using Dynamic Memory Allocation

A. Leist, K.A. Hawick

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Graph theory, Memory model, nVidia, nVidia GeForce GTX 580, Performance

December 14, 2011 by hgpu

Rethinking Runtime Verification on Hundreds of Cores: Challenges and Opportunities

Tayfun Elmas, Semih Okur, Serdar Tasiran

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 465, Performance

December 14, 2011 by hgpu

Evaluating graph coloring on GPUs

A.V. Pascal Grosset, Peihong Zhu, Shusen Liu, Suresh Venkatasubramanian, Mary Hall

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Performance, Programming techniques

December 11, 2011 by hgpu

Fast and Memory-Efficient Minimum Spanning Tree on the GPU

Scott Rostrup, Shweta Srivastava, Kishore Singhal

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Tags: Algorithms, Computer science, Graph theory, nVidia, Performance, Tesla S1070, Tesla T10

December 10, 2011 by hgpu

Objective-Driven Workload Allocation in Heterogeneous Computing Systems

Qiang Liu, Wayne Luk

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Performance, Tesla C2070

December 9, 2011 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

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high performance computing on graphics processing units: hgpu.org

Parallel programming with inductive synthesis

Massively Parallel Logic Simulation with GPUs

Customizable Memory Schemes for Data Parallel Accelerators

A Comparison of Modern GPU and CPU Architectures: And the Common Convergence of Both

Improving GPU Robustness by Making Use of Faulty Parts

Simultaneous Branch and Warp Interweaving for Sustained GPU Performance

Performance in GPU Architectures: Potentials and Distances

Graph Generation on GPUs using Dynamic Memory Allocation

Rethinking Runtime Verification on Hundreds of Cores: Challenges and Opportunities

Evaluating graph coloring on GPUs

Fast and Memory-Efficient Minimum Spanning Tree on the GPU

Objective-Driven Workload Allocation in Heterogeneous Computing Systems

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)