high performance computing on graphics processing units: hgpu.org

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Auto-tuning Streamed Applications on Intel Xeon Phi

Peng Zhang, Jianbin Fang, Tao Tang, Canqun Yang, Zheng Wang

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Tags: Computer science, Intel Xeon Phi, Machine learning, Package, Performance

May 5, 2018 by hgpu

Tiramisu: A Code Optimization Framework for High Performance Systems

Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Patricia Suriana, Shoaib Kamil, Saman Amarasinghe

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Tags: Algorithms, Code generation, Computer science, CUDA, Distributed computing, FPGA, Heterogeneous systems, Linear Algebra, LLVM, MPI, nVidia, OpenMPI, Tesla K40

May 5, 2018 by hgpu

A Survey of ReRAM-based Architectures for Processing-in-memory and Neural Networks

Sparsh Mittal

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Tags: Machine learning, memristor, neural network, processing in memory, survey

May 5, 2018 by sparsh0mittal

Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

Dan Lin

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Tags: Algorithms, Computer science, Data parallelism, LLVM, nVidia, nVidia GeForce GTX 950, Package, PTX, Thesis

April 28, 2018 by hgpu

A Comparative Study on Exact Triangle Counting Algorithms on the GPU

Leyuan Wang, Yangzihao Wang, Carl Yang, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Filtering, Graph theory, nVidia, Sparse matrix, Tesla K40

April 28, 2018 by hgpu

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Joseph D. Garvey, Tarek S. Abdelrahman

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Tags: AMD Radeon R9 390, ATI, Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance, Stencil computation

April 28, 2018 by hgpu

Accelerating Blockchain Search of Full Nodes Using GPUs

Shin Morishima, Hiroki Matsutani

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Tags: Computer science, CUDA, Finance, nVidia, nVidia GeForce GTX 980 Ti

April 28, 2018 by hgpu

Automatic generation of CUDA code performing tensor manipulations using C++ expression templates

Adam G.M. Lewis, Harald P. Pfeiffer

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Tags: Benchmarking, Code generation, Computer science, CUDA, General Relativity and Quantum Cosmology, nVidia, Tesla K80, Tesla M2090, Tesla P100

April 28, 2018 by hgpu

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

Nicolas Weber, Florian Schmidt, Mathias Niepert, Felipe Huici

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Tags: AI, Artificial intelligence, cache, CNN, Computer science, cpu, CUDA, Deep learning, GPU, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 1080 Ti

April 25, 2018 by hgpu

A Survey of Techniques for Dynamic Branch Prediction

Sparsh Mittal

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Tags: branch predictor, Paper, speculative execution, survey

April 25, 2018 by sparsh0mittal

Fast inference of deep neural networks in FPGAs for particle physics

Javier Duarte, Song Han, Philip Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Jennifer Ngadiuba, Maurizio Pierini, Nhan Tran, Zhenbin Wu

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Tags: Data acquisition, Deep learning, FPGA, HLS, Instrumentation and Detectors, Machine learning, Package, Physics

April 22, 2018 by hgpu

CANNA: Neural Network Acceleration using Configurable Approximation on GPGPU

Mohsen Imani, Max Masich, Daniel Peroni, Pushen Wang, Tajana Rosing

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Tags: AMD Radeon HD 7970, ATI, Computer science, Deep learning, Neural networks, OpenCL

April 22, 2018 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

Auto-tuning Streamed Applications on Intel Xeon Phi

Tiramisu: A Code Optimization Framework for High Performance Systems

A Survey of ReRAM-based Architectures for Processing-in-memory and Neural Networks

Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

A Comparative Study on Exact Triangle Counting Algorithms on the GPU

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Accelerating Blockchain Search of Full Nodes Using GPUs

Automatic generation of CUDA code performing tensor manipulations using C++ expression templates

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

A Survey of Techniques for Dynamic Branch Prediction

Fast inference of deep neural networks in FPGAs for particle physics

CANNA: Neural Network Acceleration using Configurable Approximation on GPGPU

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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