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Contributions to Music Semantic Analysis and Its Acceleration Techniques

Boyang Gao

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Tags: Acoustics, Algorithms, Computer science, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 580, Thesis

July 10, 2015 by hgpu

Many-Core Compiler Fuzzing

Christopher Lidbury, Andrei Lascu, Nathan Chong, Alastair F. Donaldson

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Tags: ATI, ATI Radeon HD 6570, ATI Radeon HD 7970, Compilers, Computer science, FPGA, Intel HD 4600, Intel Xeon Phi, nVidia, nVidia GeForce GTX 770, nVidia GeForce GTX Titan, OpenCL, Tesla K40, Tesla M2050

July 10, 2015 by hgpu

Towards Good Practices for Very Deep Two-Stream ConvNets

Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, MPI, nVidia, OpenMPI, Package

July 10, 2015 by hgpu

Multiple String Matching on a GPU using CUDAs

Charalampos S. Kouzinopoulos, Panagiotis D. Michailidis, Konstantinos G. Margaritis

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Tags: Algorithms, Biology, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, String matching

July 10, 2015 by hgpu

Autotuning OpenACC Work Distribution via Direct Search

Calvin Montgomery, Jeffrey L. Overbey, Xuechao Li

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Tags: Computer science, MPI, nVidia, OpenACC, Package, Performance, Python, Tesla K20

July 8, 2015 by hgpu

Learning Better Encoding for Approximate Nearest Neighbor Search with Dictionary Annealing

Shicong Liu, Hongtao Lu

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Tags: Computer science, Databases, Nearest neighbour, nVidia, nVidia GeForce GTX 980, Optimization, Vector quantization

July 8, 2015 by hgpu

Experiments on Parallel Training of Deep Neural Network using Model Averaging

Hang Su, Haoyu Chen

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Tags: Computer science, CUDA, Deep learning, MPI, Neural networks, nVidia, Tesla K20

July 8, 2015 by hgpu

High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

Anton Akusok, Kaj-Mikael Bjork, Yoan Miche, Amaury Lendasse

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, OpenCL, Package, Python

July 6, 2015 by hgpu

Best bang for your buck: GPU nodes for GROMACS biomolecular simulations

Carsten Kutzner, Szilard Pall, Martin Fechner, Ansgar Esztermann, Bert L. de Groot, Helmut Grubmuller

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Tags: Benchmarking, Biological Physics, Biomolecules, Computational Physics, Computer science, Molecular dynamics, Molecular simulation, MPI, nVidia, Package, Performance, Tesla K20, Tesla K40

July 6, 2015 by hgpu

Hetero-DB: Next Generation High-Performance Database Systems by Best Utilizing Heterogeneous Computing and Storage Resources

Kai Zhang, Feng Chen, Xiaoning Ding, Yin Huai, Rubao Lee, Tian Luo, Kaibo Wang, Yuan Yuan, Xiaodong Zhang

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Tags: Computer science, CUDA, Data parallelism, Databases, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL

July 6, 2015 by hgpu

Mega-KV: A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores

Kai Zhang, Kaibo Wang, Yuan Yuan, Lei Guo, Rubao Lee, Xiaodong Zhang

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Tags: Computer science, CUDA, Data parallelism, Databases, nVidia, nVidia GeForce GTX 780, Package

July 6, 2015 by hgpu

High Performance Approximate Sort Algorithm Using GPUs

Jun Xiao, Hao Chen, Jianhua Sun

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 780, Sorting

July 3, 2015 by hgpu

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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

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high performance computing on graphics processing units: hgpu.org

Contributions to Music Semantic Analysis and Its Acceleration Techniques

Many-Core Compiler Fuzzing

Towards Good Practices for Very Deep Two-Stream ConvNets

Multiple String Matching on a GPU using CUDAs

Autotuning OpenACC Work Distribution via Direct Search

Learning Better Encoding for Approximate Nearest Neighbor Search with Dictionary Annealing

Experiments on Parallel Training of Deep Neural Network using Model Averaging

High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

Best bang for your buck: GPU nodes for GROMACS biomolecular simulations

Hetero-DB: Next Generation High-Performance Database Systems by Best Utilizing Heterogeneous Computing and Storage Resources

Mega-KV: A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores

High Performance Approximate Sort Algorithm Using GPUs

Recent source codes

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

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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

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

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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