high performance computing on graphics processing units: hgpu.org

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Strategies for Maximizing Utilization in multi-CPU & multi-GPU Heterogeneous Architectures

Angeles Navarro, Antonio Vilches, Francisco Corbera, Rafael Asenjo

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla S2050

February 4, 2014 by hgpu

Comparing the Performance of Different x86 SIMD Instruction Sets for a Medical Imaging Application on Modern Multi- and Manycore Chips

Johannes Hofmann, Jan Treibig, Georg Hager, Gerhard Wellein

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Tags: Algorithms, Computed tomography, Computer science, CT, CUDA, Image reconstruction, Intel Xeon Phi, Medicine, nVidia, nVidia GeForce GTX 680, Performance

January 30, 2014 by hgpu

Autotuning Programs with Algorithmic Choice

Jason Ansel

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Tags: Algorithms, ATI, ATI Radeon HD 6630 M, Computer science, nVidia, OpenCL, Package, Performance, Tesla C2070, Thesis

January 28, 2014 by hgpu

Improvement of the fused CUDA kernels performance prediction

Peter Novak

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Performance, Thesis

January 25, 2014 by hgpu

clpeak – peak performance of your opencl device

Krishnaraj Bhat

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Tags: ATI, Benchmarking, Computer science, nVidia, OpenCL, Package, Performance

January 23, 2014 by hgpu

Improving the Performance of CA-GMRES on Multicores with Multiple GPUs

Ichitaro Yamazaki, Hartwig Anzt, Stanimire Tomov, Mark Hoemmenx, Jack Dongarra

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Performance, Tesla M2090

January 18, 2014 by hgpu

Performance Engineering for a Medical Imaging Application on the Intel Xeon Phi Accelerator

Johannes Hofmann, Jan Treibig, Georg Hager, Gerhard Wellein

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Tags: Benchmarking, Computed tomography, Computer science, CT, Image reconstruction, Intel Xeon Phi, Medicine, Performance

January 17, 2014 by hgpu

MRPB: Memory Request Prioritization for Massively Parallel Processors

Wenhao Jia, Kelly A. Shaw, Margaret Martonosi

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, Performance, Tesla C2050

January 16, 2014 by hgpu

Towards Portable Performance for Explicit Hydrodynamics Codes

A. C. Mallinson, D. A. Beckingsale, W. P. Gaudin, J. A. Herdman, S. A. Jarvis

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Tags: APU, ATI, ATI FirePro V7800, Computer science, Fluid dynamics, MPI, nVidia, OpenCL, Package, Performance, Tesla K20

January 14, 2014 by hgpu

Warp-Level Divergence in GPUs: Characterization, Impact, and Mitigation

Ping Xiang, Yi Yang, Huiyang Zhou

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, nVidia GeForce GTX 480, Performance

January 12, 2014 by hgpu

Conditional component composition for GPU-based systems

Usman Dastgeer, Christoph W. Kessler

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Tags: Computer science, CUDA, nVidia, Performance, Tesla C2050

January 6, 2014 by hgpu

Accelerating Simulation Codes through the GeMTC Framework

Digvijay Singh Gahlot, Scott Krieder, Ioan Raicu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Performance

January 3, 2014 by hgpu

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

NVIDIA Object Oriented Agents

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

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high performance computing on graphics processing units: hgpu.org

Strategies for Maximizing Utilization in multi-CPU & multi-GPU Heterogeneous Architectures

Comparing the Performance of Different x86 SIMD Instruction Sets for a Medical Imaging Application on Modern Multi- and Manycore Chips

Autotuning Programs with Algorithmic Choice

Improvement of the fused CUDA kernels performance prediction

clpeak – peak performance of your opencl device

Improving the Performance of CA-GMRES on Multicores with Multiple GPUs

Performance Engineering for a Medical Imaging Application on the Intel Xeon Phi Accelerator

MRPB: Memory Request Prioritization for Massively Parallel Processors

Towards Portable Performance for Explicit Hydrodynamics Codes

Warp-Level Divergence in GPUs: Characterization, Impact, and Mitigation

Conditional component composition for GPU-based systems

Accelerating Simulation Codes through the GeMTC Framework

Recent source codes

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

TensorCorePTX: PTX Tensor Core GEMM Exploration

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

NVIDIA Object Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

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