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An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

Ammar Ahmad Awan, Hari Subramoni, Dhabaleswar K. Panda

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Tags: Benchmarking, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Intel Xeon Phi, Machine learning, nVidia, Tela K40, Tesla K80, Tesla P100

December 24, 2017 by hgpu

GAMER-2: a GPU-accelerated adaptive mesh refinement code — accuracy, performance, and scalability

Hsi-Yu Schive, John A. ZuHone, Nathan J. Goldbaum, Matthew J. Turk, Massimo Gaspari, Chin-Yu Cheng

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Tags: ARM, Astrophysics, Chemistry, CUDA, Instrumentation and Methods for Astrophysics, Magnetohydrodynamics, MPI, nVidia, OpenMP, Package, Tesla K20, Tesla P100

December 24, 2017 by hgpu

Molecular dynamics recipes for genome research

Tommaso Biagini, Giovanni Chillemi, Gianluigi Mazzoccoli, Alessandro Grottesi, Caterina Fusilli, Daniele Capocefalo, Stefano Castellana, Angelo Luigi Vescovi, Tommaso Mazza

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Tags: Benchmarking, Bioinformatics, Biology, Chemistry, CUDA, Genomics, Molecular dynamics, nVidia, OpenCL, Physics, Tesla C2070

December 19, 2017 by hgpu

Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers

Lukas Polok

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Tags: Algorithms, Computer science, Differential equations, Factorization, FEM, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 680, OpenCL, Partial differential equations, PDEs, Sparse matrix, Tesla K40, Thesis

December 19, 2017 by hgpu

OpenCL-accelerated Point Feature Histogram and Its Application in Railway Track Point Cloud Data Processing

Dongxu Lv, Peijun Wang, Wentao Li, Peng Chen

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Tags: Algorithms, AMD Radeon R7 260, ATI, Computer science, OpenCL

December 19, 2017 by hgpu

Tactics to Directly Map CNN graphs on Embedded FPGAs

Kamel Abdelouahab, Maxime Pelcat, Cedric Bourrasset, Francois Berry, Jocelyn Serot

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Tags: Caffe, Computer science, Deep learning, FPGA, Neural networks, Package

December 19, 2017 by hgpu

Improving 3D Lattice Boltzmann Method stencil with asynchronous transfers on many-core processors

Minh Quan Ho, Christian Obrecht, Bernard Tourancheau, Benoit Dupont de Dinechin, Julien Hascoet

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Tags: cfd, Fluid dynamics, Lattice Boltzmann model, OpenCL, Stencil computation

December 19, 2017 by hgpu

POMPEI: Programming with OpenMP4 for Exascale Investigations

Jack Dongarra, Azzam Haidar, Oscar Hernandez, Stanimire Tomov, Manjunath Gorentla Venkata

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, nVidia, OpenACC, OpenMP, Programming techniques, Tesla M2090

December 15, 2017 by hgpu

Intra-node Memory Safe GPU Co-Scheduling

Carlos Reano, Federico Silla, Dimitrios S. Nikolopoulos, Blesson Varghese

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Tags: Computer science, CUDA, nVidia, Package, Task scheduling, Tesla K20

December 15, 2017 by hgpu

Effective Extensible Programming: Unleashing Julia on GPUs

Tim Besard, Christophe Foket, Bjorn De Sutter

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Tags: Code generation, Computer science, CUDA, High-level Languages, LLVM, nVidia, nVidia GeForce GTX 1080, Package, Programming Languages

December 15, 2017 by hgpu

Task Scheduling for Heterogeneous Multicore Systems

Zhuo Chen, Diana Marculescu

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 760 Ti, OpenCL, Task scheduling

December 15, 2017 by hgpu

A Survey of Techniques for Architecting SLC/MLC/TLC Hybrid Flash Memory based SSDs

Ahmed I Alsalibi, Sparsh Mittal, Mohammed Azmi Al-betar and Putra Bin Sumari

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Tags: Flash memory, Memory

December 14, 2017 by sparsh0mittal

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

Applications

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

GAMER-2: a GPU-accelerated adaptive mesh refinement code — accuracy, performance, and scalability

Molecular dynamics recipes for genome research

Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers

OpenCL-accelerated Point Feature Histogram and Its Application in Railway Track Point Cloud Data Processing

Tactics to Directly Map CNN graphs on Embedded FPGAs

Improving 3D Lattice Boltzmann Method stencil with asynchronous transfers on many-core processors

POMPEI: Programming with OpenMP4 for Exascale Investigations

Intra-node Memory Safe GPU Co-Scheduling

Effective Extensible Programming: Unleashing Julia on GPUs

Task Scheduling for Heterogeneous Multicore Systems

A Survey of Techniques for Architecting SLC/MLC/TLC Hybrid Flash Memory based SSDs

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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