high performance computing on graphics processing units: hgpu.org

James Bergstra, Frederic Bastien, Olivier Breuleux, Pascal Lamblin, Razvan Pascanu, Olivier Delalleau, Guillaume Desjardins, David Warde-Farley, Ian Goodfellow, Arnaud Bergeron, Yoshua Bengio

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Tags: Algorithms, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Package, Python

January 16, 2012 by hgpu

Data Triage and Visual Analytics for Scientific Visualization

Teng-Yok Lee

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Tags: Computer science, CUDA, GLSL, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, nVidia GeForce GTX 460, Thesis, Visualization

January 16, 2012 by hgpu

Parallel AES Encryption Engines for Many-Core Processor Arrays

Bin Liu, Bevan M. Baas

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Tags: AES, Computer science, DSP, nVidia, nVidia GeForce 8800 GTX, Security

January 16, 2012 by hgpu

Nested Intervals Tree Encoding with System of Residual Classes

A. Malikov, A. Turyev

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia

January 16, 2012 by hgpu

Spectral Method Characterization on FPGA and GPU Accelerators

Karl Pereira, Peter Athanas, Heshan Lin, Wu Feng

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Tags: Computer science, CUDA, DSP, FFT, FPGA, nVidia, nVidia GeForce GTX 280, Performance, Tesla C2050

January 15, 2012 by hgpu

Enhancing Performance for Solving Finite Element Mesh using Heterogeneous Platforms

Muhammad Hasan Jamal, Nabeel AlSaber

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Tags: Computer science, CUDA, FEM, Finite element method, Heterogeneous systems, Linear Algebra, nVidia, nVidia Quadro FX 5600

January 15, 2012 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Closing the Ninja Performance Gap through Traditional Programming and Compiler Technology

A Template Metaprogramming Approach to Support Parallel Programs for Multicores

Finding Convex Hulls Using Quickhull on the GPU

Programming on Parallel Machines: GPU, Multicore, Clusters and More

FPGA Based Acceleration of Decimal Operations

A Modular System Architecture for Online Parallel Vision Pipelines

Theano: Deep Learning on GPUs with Python

Data Triage and Visual Analytics for Scientific Visualization

Parallel AES Encryption Engines for Many-Core Processor Arrays

Nested Intervals Tree Encoding with System of Residual Classes

Spectral Method Characterization on FPGA and GPU Accelerators

Enhancing Performance for Solving Finite Element Mesh using Heterogeneous Platforms

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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