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Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing

Seyong Lee, Dong Li, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, nVidia, OpenACC, Performance, Tesla M2090

March 7, 2014 by hgpu

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

M.G.B. Johnson, D. P. Playne, K.A. Hawick

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 660 M, nVidia GeForce GTX 680, Performance, Tesla M2050, Tesla M2070, Tesla M2075, Tesla M2090

March 6, 2014 by hgpu

A Similarity-Based Analysis Tool for Scientific Application Porting

Wei Ding

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Tags: Benchmarking, Computer science, CUDA, MPI, nVidia, OpenMP, Performance, Portability, Tesla C2070, Thesis

February 17, 2014 by hgpu

Accelerator Aware MPI Micro-benchmarking using CUDA, OpenACC and OpenCL

Sadaf Alam, Ugo Varetto

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, MPI, nVidia, OpenACC, OpenCL, Performance, Tesla K20, Tesla M2090

February 15, 2014 by hgpu

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

M. Bernaschi, M. Bisson, F. Salvadore

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Source codes

Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, Physics, Spin Systems, Tesla K20

February 14, 2014 by hgpu

Benchmarking the Intel Xeon Phi Coprocessor

F. Masci

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Performance

February 11, 2014 by hgpu

Benchmarks for Intel MIC Architecture

Emprise Y.K. Chan

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Monte Carlo simulation

February 9, 2014 by hgpu

Developmental Directions in Parallel Accelerators

K.A. Hawick, D.P. Playne

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan

February 8, 2014 by hgpu

Simulating and Benchmarking the Shallow-Water Fluid Dynamical Equations on Multiple Graphical Processing Units

D.P. Playne, K.A. Hawick, M.G.B. Johnson

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Tags: Benchmarking, CUDA, Fluid dynamics, nVidia, Tesla K20

February 8, 2014 by hgpu

clpeak – peak performance of your opencl device

Krishnaraj Bhat

Source codes

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

January 23, 2014 by hgpu

Supporting x86-64 Address Translation for 100s of GPU Lanes

Jason Power, Mark D. Hill, David A. Wood

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Tags: Benchmarking, Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia

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

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

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high performance computing on graphics processing units: hgpu.org

Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

A Similarity-Based Analysis Tool for Scientific Application Porting

Accelerator Aware MPI Micro-benchmarking using CUDA, OpenACC and OpenCL

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

Benchmarking the Intel Xeon Phi Coprocessor

Benchmarks for Intel MIC Architecture

Developmental Directions in Parallel Accelerators

Simulating and Benchmarking the Shallow-Water Fluid Dynamical Equations on Multiple Graphical Processing Units

clpeak – peak performance of your opencl device

Supporting x86-64 Address Translation for 100s of GPU Lanes

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

Recent source codes

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

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