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The Potential of the Intel Xeon Phi for Supervised Deep Learning

Andre Viebke, Sabri Pllana

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Tags: CNN, Computer science, Deep learning, Intel Xeon Phi, Neural networks, OpenMP, Performance

July 1, 2015 by hgpu

Accelerating DNA Sequence Analysis using Intel Xeon Phi

Suejb Memeti, Sabri Pllana

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Tags: Bioinformatics, Computer science, Intel Xeon Phi, OpenMP, Performance

July 1, 2015 by hgpu

Study of Sparse-Matrix Vector Multiplication (SpMV) on Different Architectures and Libraries

Naveen Anand Subramaniam, Omkar Deshmukh, Vennila Megavannan, Dan Negrut

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 770, OpenCL, Performance, Sparse matrix, Tesla K20, Tesla K40

June 19, 2015 by hgpu

Visualization of OpenCL Application Execution on CPU-GPU Systems

Amir Kavyan Ziabari, Rafael Ubal, Dana Schaa, David Kaeli

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Tags: ARM, ATI, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Performance, Visualization

June 19, 2015 by hgpu

Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs

N. Sedaghati, A. Ashari, L. N. Pouchet, S. Parthasarathy, P. Sadayappan

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Performance, Sparse matrix, Tesla K40

June 16, 2015 by hgpu

Performance of a code migration for the simulation of supersonic ejector flow to SMP, MIC and GPU using OpenMP, OpenMP+LEO, and OpenACC directives

C. Couder-Castaneda, H. Barrios-Pina, I. Gitler, M. Arroyo

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Tags: Computer science, Fortran, Intel Xeon Phi, nVidia, OpenACC, OpenMP, Performance, Tesla C2070

June 10, 2015 by hgpu

Meta-Programming and Auto-Tuning in the Search for High Performance GPU Code

Michael Vollmer, Bo Joel Svensson, Eric Holk, Ryan Newton

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Tags: Computer science, CUDA, nVidia, Performance, Search strategies, Tesla K40

June 7, 2015 by hgpu

Analysis of GPU Parallel Computing based on Matlab

Mingzhe Wang, Bo Wang, Qiu He, Xiuxiu Liu, Kunshuai Zhu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 540 M, Performance

May 28, 2015 by hgpu

A Performance and Scalability Analysis of the Tsunami Simulation EasyWave for Different Multi-Core Architectures and Programming Models

Steffen Christgau, Johannes Spazier, Bettina Schnor

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Tags: CUDA, Earth and Space Sciences, Geoscience, Intel Xeon Phi, MPI, nVidia, OpenMP, Package, Performance, Tesla K40

May 20, 2015 by hgpu

An Efficient, Automatic Approach to High Performance Heterogeneous Computing

Gordon Inggs, David B. Thomas, Wayne Luk

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Tags: AMD FirePro W5000, ATI, Computational finance, Computer science, Finance, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Grid K2, nVidia Quadro K4000, OpenCL, Option pricing, Performance

May 20, 2015 by hgpu

CHO: Towards a Benchmark Suite for OpenCL FPGA Accelerators

Geoffrey Ndu, Javier Navaridas, Mikel Lujan

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Tags: Benchmarking, Computer science, FPGA, OpenCL, Package, Performance

May 19, 2015 by hgpu

Optimizing Full Correlation Matrix Analysis of fMRI Data on Intel Xeon Phi Coprocessors

Yida Wang, Michael Anderson, Jonathan D. Cohen, Alexander Heinecke, Kai Li, Nadathur Satish, Narayanan Sundaram, Nicholas B. Turk-Browne, Ted Willke

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Tags: Algorithms, Computer science, Intel Xeon Phi, Magnetic resonance imaging, MRI, Performance

May 19, 2015 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

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high performance computing on graphics processing units: hgpu.org

The Potential of the Intel Xeon Phi for Supervised Deep Learning

Accelerating DNA Sequence Analysis using Intel Xeon Phi

Study of Sparse-Matrix Vector Multiplication (SpMV) on Different Architectures and Libraries

Visualization of OpenCL Application Execution on CPU-GPU Systems

Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs

Performance of a code migration for the simulation of supersonic ejector flow to SMP, MIC and GPU using OpenMP, OpenMP+LEO, and OpenACC directives

Meta-Programming and Auto-Tuning in the Search for High Performance GPU Code

Analysis of GPU Parallel Computing based on Matlab

A Performance and Scalability Analysis of the Tsunami Simulation EasyWave for Different Multi-Core Architectures and Programming Models

An Efficient, Automatic Approach to High Performance Heterogeneous Computing

CHO: Towards a Benchmark Suite for OpenCL FPGA Accelerators

Optimizing Full Correlation Matrix Analysis of fMRI Data on Intel Xeon Phi Coprocessors

Recent source codes

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

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