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Advanced Trends of Heterogeneous Computing with CPU-GPU Integration: Comparative Study

Ishan Rajani, G Nanda Gopal

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Tags: AMD FireStream 9270, ATI, Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, OpenCL, Tesla C2050, Warp level parallelism

January 26, 2013 by hgpu

Autotuning, Code Generation and Optimizing Compiler Technology for GPUs

Malik Muhammad Zaki Murtaza Khan

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Tags: Code generation, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2050, Thesis

January 26, 2013 by hgpu

Orthogonalization on a General Purpose Graphics Processing Unit with Double Double and Quad Double Arithmetic

Jan Verschelde, Genady Yoffe

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Tags: Algorithms, CUDA, Extended precision, Mathematics, nVidia, Tesla C2050

January 25, 2013 by hgpu

Locality-Aware Work Stealing on Multi-CPU and Multi-GPU Architectures

Thierry Gautier, Joao V. F. Lima, Nicolas Maillard, Bruno Raffin

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Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, Package, Tesla C2050

January 25, 2013 by hgpu

Programmability and Performance Portability Aspects of Heterogeneous Multi-/Manycore Systems

Christoph Kessler, Usman Dastgeer, Samuel Thibault, Raymond Namyst, Andrew Richards, Uwe Dolinsky, Siegfried Benkner, Jesper Larsson Traff, Sabri Pllana

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Performance, Tesla C1060, Tesla C2050

January 24, 2013 by hgpu

Data parallel patterns on CPU/GPU mix

T. Serban, M. Danelutto, M. Coppola

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GT 540, nVidia GeForce GTX 285, Tesla C2050

January 22, 2013 by hgpu

The PEPPHER Composition Tool: Performance-Aware Dynamic Composition of Applications for GPU-based Systems

Usman Dastgeer, Lu Li, Christoph Kessler

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C1060, Tesla C2050

January 22, 2013 by hgpu

Duality based optical flow algorithms with applications

Lars Lau Raket

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Tags: Computer science, Computer vision, CUDA, nVidia, Optical flow, Tesla C2050

January 19, 2013 by hgpu

Three Dimensional Fast Fourier Transform CUDA Implementation

Kumar Aatish, Boyan Zhang

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Tags: Algorithms, Computer science, CUDA, FFT, nVidia, Tesla C2050

January 15, 2013 by hgpu

Two Approaches to Particle Simulation: OpenMPI and CUDA

Bjorn Christian Seime, Matteo Mannino, Terje Runde

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Tags: Computer science, CUDA, MPI, N-body simulation, nVidia, nVidia GeForce GTX 280, OpenMPI, Particle simulation, Tesla C2050

January 15, 2013 by hgpu

Automatic scanning of nuclear emulsions with wide-angle acceptance for nuclear fragment detection

T. Fukuda, S. Fukunaga, H. Ishida, K. Kodama, T. Matsuo, S. Mikado, S. Ogawa, H. Shibuya, J. Sudo

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Tags: CUDA, High Energy Physics - Experiment, Nuclear Experiment, Nuclear physics, nVidia, nVidia GeForce GTX 680, Physics, Tesla C2050

January 11, 2013 by hgpu

Parallel Computing of Discrete Element Method on GPU

Teruyoshi Washizawa, Yasuhiro Nakahara

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Tags: Computational Engineering, Computer science, CUDA, nVidia, OpenCL, Tesla C2050

January 11, 2013 by hgpu

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Advanced Trends of Heterogeneous Computing with CPU-GPU Integration: Comparative Study

Autotuning, Code Generation and Optimizing Compiler Technology for GPUs

Orthogonalization on a General Purpose Graphics Processing Unit with Double Double and Quad Double Arithmetic

Locality-Aware Work Stealing on Multi-CPU and Multi-GPU Architectures

Programmability and Performance Portability Aspects of Heterogeneous Multi-/Manycore Systems

Data parallel patterns on CPU/GPU mix

The PEPPHER Composition Tool: Performance-Aware Dynamic Composition of Applications for GPU-based Systems

Duality based optical flow algorithms with applications

Three Dimensional Fast Fourier Transform CUDA Implementation

Two Approaches to Particle Simulation: OpenMPI and CUDA

Automatic scanning of nuclear emulsions with wide-angle acceptance for nuclear fragment detection

Parallel Computing of Discrete Element Method on GPU

Recent source codes

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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