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hgpu.org » Programming » OpenCL

Out-of-the-box library support for DBMS operations on GPUs

Harish Kumar Harihara Subramanian, Bala Gurumurthy, Gabriel Campero Durand, David Broneske, Gunter Saake

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Tags: Computer science, Databases, Hashing, Heterogeneous systems, nVidia, nVidia GeForce RTX 2080 Ti, nVidia V100, OpenCL, Package, Performance

July 2, 2023 by hgpu

Deep Language Models for Software Testing and Optimisation

Foivos Tsimpourlas

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Tags: Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce Titan X, OpenCL, Package, Software Engineering, Thesis

June 25, 2023 by hgpu

cuCatch: A Debugging Tool for Efficiently Catching Memory Safety Violations in CUDA Applications

Mohamed Tarek Ibn Ziad, Sana Damani, Aamer Jaleel, Stephen W. Keckler, Mark Stephenson

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, OpenACC, OpenCL, Security

June 18, 2023 by hgpu

GPUHarbor: Testing GPU Memory Consistency at Large

Reese Levine, Mingun Cho, Devon McKee, Andrew Quinn, Tyler Sorensen

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Tags: AMD Radeon Pro 5500M, ATI, Computer science, Memory model, nVidia, nVidia GeForce RTX 3080, nVidia Tegra TX1, OpenCL, Performance

June 11, 2023 by hgpu

minimap2-fpga: Integrating hardware-accelerated chaining for efficient end-to-end long-read sequence mapping

Kisaru Liyanage, Hiruna Samarakoon, Sri Parameswaran, Hasindu Gamaarachchi

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Tags: Bioinformatics, Biology, Cloud, FPGA, OpenCL, Package, Sequence alignment

June 11, 2023 by hgpu

Accelerating 128-bit Floating-Point Matrix Multiplication on FPGAs

Fumiya Kono, Naohito Nakasato, Maho Nakata

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Tags: Benchmarking, Computer science, Extended precision, FPGA, Matrix multiplication, OpenCL

June 11, 2023 by hgpu

Compiler Technologies in Deep Learning Co-Design: A Survey

Hongbin Zhang, Mingjie Xing, Yanjun Wu, Chen Zhao

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Tags: Compielrs, Computer science, Deep learning, HLS, OpenCL, Package, survey

June 4, 2023 by hgpu

Implementation Techniques for SPMD Kernels on CPUs

Joachim Meyer, Aksel Alpay, Sebastian Hack, Holger Fröning, Vincent Heuveline

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, performance portability

June 4, 2023 by hgpu

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

Juan Fumero, György Rethy, Athanasios Stratikopoulos, Nikos Foutris, Christos Kotselidis

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Tags: Code generation, Computer science, Heterogeneous systems, Java, OpenCL, Package

May 21, 2023 by hgpu

Towards Alignment of Parallelism in SYCL and ISO C++

Simon John Pennycook, Ben Ashbaugh, James Brodman, Michael Kinsner, Steffen Larsen, Greg Lueck, Roland Schulz, Michael Voss

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Tags: Computer science, OpenCL, Performance, SYCL

May 14, 2023 by hgpu

Performance Optimization using Multimodal Modeling and Heterogeneous GNN

Akash Dutta, Jordi Alcaraz, Ali TehraniJamsaz, Eduardo Cesar, Anna Sikora, Ali Jannesari

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Tags: AMD Radeon HD 7970, ATI, Benchmarking, Computer science, Deep learning, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 970, OpenCL, OpenMP

May 14, 2023 by hgpu

Descend: A Safe GPU Systems Programming Language

Bastian Köpcke, Sergei Gorlatch, Michel Steuwer

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Programming Languages, Tesla P100

May 14, 2023 by hgpu

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

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

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high performance computing on graphics processing units: hgpu.org

Programming

Out-of-the-box library support for DBMS operations on GPUs

Deep Language Models for Software Testing and Optimisation

cuCatch: A Debugging Tool for Efficiently Catching Memory Safety Violations in CUDA Applications

GPUHarbor: Testing GPU Memory Consistency at Large

Accelerating 128-bit Floating-Point Matrix Multiplication on FPGAs

Compiler Technologies in Deep Learning Co-Design: A Survey

Implementation Techniques for SPMD Kernels on CPUs

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

Towards Alignment of Parallelism in SYCL and ISO C++

Performance Optimization using Multimodal Modeling and Heterogeneous GNN

Descend: A Safe GPU Systems Programming Language

Recent source codes

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

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Awesome LLM-Driven Kernel Generation

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