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Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Jesus Perez Serrano, Edans Flavius De Oliveira Sandes, Alba Cristina Magalhaes Alves de Melo, Manuel Ujaldon

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Tags: Bioinformatics, Biology, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 980, Performance, Smith-Waterman algorithm

June 10, 2017 by hgpu

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

Qingqing Cao, Niranjan Balasubramanian, Aruna Balasubramanian

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Tags: Computer science, CUDA, Deep learning, Java, Neural networks, nVidia, Package, RNN, TensorFlow

June 10, 2017 by hgpu

CELES: CUDA-accelerated simulation of electromagnetic scattering by large ensembles of spheres

Amos Egel, Lorenzo Pattelli, Giacomo Mazzamuto, Diederik S. Wiersma, Uli Lemmer

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Tags: Computational Physics, CUDA, Matlab, nVidia, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan X, Optics, Package, Physics

June 10, 2017 by hgpu

Neneta: Heterogeneous Computing Complex-Valued Neural Network Framework

Vladimir Lekic, Zdenka Babic

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Tags: ATI, ATI Radeon HD 5770, Computer science, Deep learning, Heterogeneous systems, Neural networks, OpenCL

June 5, 2017 by hgpu

Speedup and Parallelization Models for Energy-Efficient Many-Core Systems Using Performance Counters

Mohammed A. Noaman Al-hayanni, Rishad Shafik, Ashur Rafiev, Fei Xia, Alex Yakovlev

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, Performance

June 5, 2017 by hgpu

Program Acceleration in a Heterogeneous Computing Environment Using OpenCL, FPGA, and CPU

Herman Noel Hoffman

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL, SoC, Thesis

June 5, 2017 by hgpu

UT-OCL: An OpenCL Framework for Embedded Systems Using Xilinx FPGAs

Vincent Mirian

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL, SoC, Thesis

June 5, 2017 by hgpu

A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic Problems Leveraging the Graphics Processor Unit

Mark W. Lewis

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Tags: Algorithms, Artificial intelligence, Benchmarking, Computer science, CUDA, Metaheuristics, nVidia, nVidia GeForce GTS 780 Ti

June 5, 2017 by hgpu

Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing

Lingyu Duan, Wei Sun, Xinfeng Zhang, Jie Chen, Jianxiong Yin, Simon See, Tiejun Huang, Alex C. Kot, Wen Gao

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Tags: Benchmarking, CUDA, Hybrid computing, Image processing, Neural networks, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, nVidia Tegra TX1, Tesla M40

June 1, 2017 by hgpu

A performance spectrum for parallel computational frameworks that solve PDEs

J. Chang, K. B. Nakshatrala, M. G. Knepley, L. Johnsson

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Tags: Computational Physics, Computer science, Intel Xeon Phi, Mathematical Software, MPI, Partial differential equations, PDEs

June 1, 2017 by hgpu

SparkJNI: A Reference Design for a Heterogeneous Apache Spark Framework

Tudor Alexandru Voicu

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Tags: big data, Computer science, FPGA, Heterogeneous systems, Hybrid computing, Java, OpenCL, Spark, Thesis

June 1, 2017 by hgpu

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

Xiaoming Chen, Jianxu Chen, Danny Z. Chen, Xiaobo Sharon Hu

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Tesla K40

June 1, 2017 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

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

Applications

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

CELES: CUDA-accelerated simulation of electromagnetic scattering by large ensembles of spheres

Neneta: Heterogeneous Computing Complex-Valued Neural Network Framework

Speedup and Parallelization Models for Energy-Efficient Many-Core Systems Using Performance Counters

Program Acceleration in a Heterogeneous Computing Environment Using OpenCL, FPGA, and CPU

UT-OCL: An OpenCL Framework for Embedded Systems Using Xilinx FPGAs

A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic Problems Leveraging the Graphics Processor Unit

Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing

A performance spectrum for parallel computational frameworks that solve PDEs

SparkJNI: A Reference Design for a Heterogeneous Apache Spark Framework

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

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

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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