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Meta-programming and Multi-stage Programming for GPGPUs

Marc Baboulin, Joel Falcou, Ian Masliah

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Tags: Computer science, CUDA, nVidia, Tesla K40

October 11, 2015 by hgpu

Performance Analysis of an Astrophysical Simulation Code on the Intel Xeon Phi Architecture

Vahid Noormofidi, Susan R. Atlas, Huaiyu Duan

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Tags: Astrophysics, Computer science, Intel Xeon Phi, MPI, OpenMP, Performance

October 11, 2015 by hgpu

GPU acceleration of preconditioned solvers for ill-conditioned linear systems

Rohit Gupta

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Tags: Computer science, Conjugate gradient solver, MPI, nVidia, Tesla C2070, Thesis

October 11, 2015 by hgpu

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit

Felix Weninger, Johannes Bergmann, Bjorn Schuller

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Tags: Benchmarking, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 560, Package, Speech recognition

October 8, 2015 by hgpu

GPU-Based Computation of 2D Least Median of Squares with Applications to Fast and Robust Line Detection

Gil Shapira, Tal Hassner

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Tags: Algorithms, Computer science, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 720, Package

October 8, 2015 by hgpu

Solving the Quadratic Assignment Problem on heterogeneous environment (CPUs and GPUs) with the application of Level 2 Reformulation and Linearization Technique

Alexandre Domingues Goncalves, Artur Alves Pessoa, Lucia Maria de Assumpcao Drummond, Cristiana Bentes, Ricardo Farias

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, Tesla C2070

October 8, 2015 by hgpu

Exploiting Task-Parallelism on GPU Clusters via OmpSs and rCUDA Virtualization

Adrian Castello, Rafael Mayo, Judit Planas, Enrique S. Quintana-Orti

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Tags: Computer science, CUDA, GPU cluster, nVidia, OmpSs, Tesla C2050, Tesla M2050, Virtualization

October 8, 2015 by hgpu

MAGMA Embedded: Towards a Dense Linear Algebra Library for Energy Efficient Extreme Computing

Azzam Haidar, Stanimire Tomov, Piotr Luszczek, Jack Dongarra

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Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, nVidia Jetson TK1, Package

October 6, 2015 by hgpu

Parallel Graph Algorithms on the Xeon Phi Coprocessor

Dennis Felsing

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Tags: Algorithms, Computer science, Graph theory, Intel Xeon Phi, OpenMP, Thesis, Visualization

October 6, 2015 by hgpu

Optimizing GPU-accelerated Group-By and Aggregation

Tomas Karnagel, Rene Mueller, Guy M. Lohman

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX Titan

October 6, 2015 by hgpu

A Toolkit for Building Dynamic Compilers for Array-Based Languages Targeting CPUs and GPUs

Rahul Garg

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Tags: AMD Radeon HD 8650, ATI, ATI Radeon HD 7970, Benchmarking, Code generation, Computer science, Intel HD Graphics 4000, Matlab, Matrix multiplication, nVidia, nVidia GeForce GT 650 M, OpenCL, Package, Python, Tesla C2050, Thesis

October 6, 2015 by hgpu

Brute-Force k-Nearest Neighbors Search on the GPU

Shengren Li, Nina Amenta

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Tags: Computer science, CUDA, Matrix multiplication, Nearest neighbour, nVidia, nVidia GeForce GTX 460, Sorting

October 3, 2015 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

Applications

Meta-programming and Multi-stage Programming for GPGPUs

Performance Analysis of an Astrophysical Simulation Code on the Intel Xeon Phi Architecture

GPU acceleration of preconditioned solvers for ill-conditioned linear systems

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit

GPU-Based Computation of 2D Least Median of Squares with Applications to Fast and Robust Line Detection

Solving the Quadratic Assignment Problem on heterogeneous environment (CPUs and GPUs) with the application of Level 2 Reformulation and Linearization Technique

Exploiting Task-Parallelism on GPU Clusters via OmpSs and rCUDA Virtualization

MAGMA Embedded: Towards a Dense Linear Algebra Library for Energy Efficient Extreme Computing

Parallel Graph Algorithms on the Xeon Phi Coprocessor

Optimizing GPU-accelerated Group-By and Aggregation

A Toolkit for Building Dynamic Compilers for Array-Based Languages Targeting CPUs and GPUs

Brute-Force k-Nearest Neighbors Search on the GPU

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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