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SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures

Alexandros Koliousis, Matthias Weidlich, Raul Castro Fernandez, Alexander L. Wolf, Paolo Costa, Peter Pietzuch

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Tags: Computer science, Databases, Heterogeneous systems, nVidia, nVidia Quadro K5200, OpenCL

February 16, 2016 by hgpu

CaffeLink: Mathematica binding for Caffe Deep Learning Framework

Martin Kerhart, Jan Drchal

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Tags: Caffe, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Package

February 16, 2016 by hgpu

Towards Improving Programmability of Heterogeneous Parallel Architectures

Michele Scandale

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Tags: Compilers, Computer science, Heterogeneous systems, LLVM, OpenCL, Thesis

February 16, 2016 by hgpu

Parallel and Scalable Sparse Basic Linear Algebra Subprograms

Weifeng Liu

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Tags: Algorithms, AMD Radeon R9 290X, ATI, Benchmarking, Computer science, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan Black, OpenCL, Sparse matrix, Thesis

February 16, 2016 by hgpu

Writing a performance-portable matrix multiplication

Jorge F. Fabeiro, Diego Andrade, Basilio B. Fraguela

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Tags: AMD FirePro S9150, ATI, Code generation, Computer science, Heterogeneous systems, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package, Performance, performance portability, Tesla K20

February 11, 2016 by hgpu

Programming GPUs with C++14 and Just-In-Time Compilation

Michael Haidl, Bastian Hagedorn, Sergei Gorlatch

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Tags: Compilers, Computer science, CUDA, Intel Xeon Phi, LLVM, nVidia, OpenCL, PTX, Tesla K20

February 10, 2016 by hgpu

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Matthieu Courbariaux, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

February 10, 2016 by hgpu

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Nachiket Kapre, Deheng Ye

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Tags: Benchmarking, Code generation, Computer science, CUDA, FPGA, nVidia, Package, Performance, Tesla K20

February 10, 2016 by hgpu

FARGO3D: A new GPU-oriented MHD code

Pablo Benitez-Llambay, Frederic Masset

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Tags: Astrophysics, CUDA, Finite difference, Instrumentation and Methods for Astrophysics, Magnetohydrodynamics, MPI, N-body simulation, nVidia, Package, Physics, Tesla K20

February 10, 2016 by hgpu

Performance Portable GPU Code Generation for Matrix Multiplication

Toomas Remmelg, Thibaut Lutz, Michel Steuwer, Christophe Dubach

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Tags: ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Matrix multiplication, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX Titan Black, OpenCL

February 10, 2016 by hgpu

Guided Profiling for Auto-Tuning Array Layouts on GPUs

Nicolas Weber, Sandra C. Amend and Michael Goesele

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Tags: Array Layouts, Auto-Tuning, CUDA, Profiling

February 9, 2016 by mergian

Portable Programming Models for Heterogeneous Platforms

Deepak Majeti

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7660, Computer science, Heterogeneous systems, nVidia, OpenCL, Package, Tesla M2050, Thesis

February 8, 2016 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

Applications

SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures

CaffeLink: Mathematica binding for Caffe Deep Learning Framework

Towards Improving Programmability of Heterogeneous Parallel Architectures

Parallel and Scalable Sparse Basic Linear Algebra Subprograms

Writing a performance-portable matrix multiplication

Programming GPUs with C++14 and Just-In-Time Compilation

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

FARGO3D: A new GPU-oriented MHD code

Performance Portable GPU Code Generation for Matrix Multiplication

Guided Profiling for Auto-Tuning Array Layouts on GPUs

Portable Programming Models for Heterogeneous Platforms

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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