high performance computing on graphics processing units: hgpu.org

hgpu.org » Optimization

A case for neuromorphic ISAs

Atif Hashmi, Andrew Nere, James Jamal Thomas, Mikko Lipasti

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Tags: Algorithms, Computer science, CUDA, Java, MPI, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, Optimization, Performance

September 22, 2011 by hgpu

Multicore performance optimization using partner cores

Eric Lau, Jason E. Miller, Inseok Choi, Donald Yeung, Saman Amarasinghe, Anant Agarwal

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Tags: Computer science, Optimization, Programming techniques

September 21, 2011 by hgpu

Mint: realizing CUDA performance in 3D stencil methods with annotated C

Didem Unat, Xing Cai, Scott B. Baden

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia, OpenMP, Optimization, Package, Performance, Programming Languages, Tesla C1060

September 21, 2011 by hgpu

Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors

Ashwin Prasad, Jayvant Anantpur, R. Govindarajan

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Tags: Benchmarking, Clustering, Computer science, CUDA, Data parallelism, Flow analysis, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTS, Optimization

September 20, 2011 by hgpu

DAMS: distributed adaptive metaheuristic selection

Bilel Derbel, Sebastien Verel

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Tags: Algorithms, Computer science, Metaheuristics, Optimization, Presentation

September 19, 2011 by hgpu

ACO with tabu search on a GPU for solving QAPs using move-cost adjusted thread assignment

Shigeyoshi Tsutsui, Noriyuki Fujimoto

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Optimization, Search

September 19, 2011 by hgpu

Randomized selection on the GPU

Laura Monroe, Joanne Wendelberger, Sarah Michalak

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, nVidia Quadro FX 5000, nVidia Quadro FX 6000, Optimization, Presentation, Probability, Programming techniques

September 19, 2011 by hgpu

A GPU-tailored approach for training kernelized SVMs

Andrew Cotter, Nathan Srebro, Joseph Keshet

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Tags: Algorithms, Clustering, Computer science, nVidia, Optimization, Package, Performance, Programming techniques, Tesla C1060

September 19, 2011 by hgpu

cuIBM — A GPU-accelerated Immersed Boundary Method

Simon K Layton, Anush Krishnan, Lorena A. Barba

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Tags: CUDA, Fluid dynamics, Linear Algebra, nVidia, Optimization, Tesla C2050

September 19, 2011 by hgpu

Returning control to the programmer: SIMD intrinsics for virtual machines

Jonathan Parri, Daniel Shapiro, Miodrag Bolic, Voicu Groza

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Tags: Computer science, Java, Optimization, Programming Languages

September 16, 2011 by hgpu

Optimizations and Performance of a Robotics Grasping Algorithm Described in Geometric Algebra

Florian Worsdorfer, Florian Stock, Eduardo Bayro-Corrochano, Dietmar Hildenbrand

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Optimization, Package, Performance

September 16, 2011 by hgpu

Fast Mersenne prime testing on the GPU

Andrew Thall

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Tags: Algorithms, Benchmarking, CUDA, Mathematical Software, Mathematics, nVidia, nVidia GeForce GTX 480, Optimization, Performance, Tesla C2050

September 15, 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

A case for neuromorphic ISAs

Multicore performance optimization using partner cores

Mint: realizing CUDA performance in 3D stencil methods with annotated C

Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors

DAMS: distributed adaptive metaheuristic selection

ACO with tabu search on a GPU for solving QAPs using move-cost adjusted thread assignment

Randomized selection on the GPU

A GPU-tailored approach for training kernelized SVMs

cuIBM — A GPU-accelerated Immersed Boundary Method

Returning control to the programmer: SIMD intrinsics for virtual machines

Optimizations and Performance of a Robotics Grasping Algorithm Described in Geometric Algebra

Fast Mersenne prime testing on the GPU

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)