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Fast and accurate digital signal processing realized with GPGPU technology

Adam Dabrowski, Pawel Pawlowski, Mateusz Stankiewicz, Filip Misiorek

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Tags: CUDA, nVidia, nVidia Quadro FX 2000, nVidia Quadro FX 580, Signal processing

May 26, 2012 by hgpu

Parallelization of the Local Threshold and Boolean Function Based Edge Detection Algorithm Using CUDA

Raka Jovanovic, Milan Tuba, Dana Simian

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 540 M

May 26, 2012 by hgpu

Using Compute Unified Device Architecture (CUDA) in Parallelizing Different Digital Image Processing Techniques

Mikaela Nadinne A. Silapan

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Tags: CUDA, Image processing, nVidia, nVidia GeForce GT 520 M

May 25, 2012 by hgpu

On the Simulations of Evolution-Communication P Systems with Energy without Antiport Rules for GPUs

Richelle Ann B. Juayong, Francis George C. Cabarle, Henry N. Adorna, Miguel A. Martinez-del-Amor

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Tags: Computer science, CUDA, Membrane computing, nVidia, P systems

May 25, 2012 by hgpu

Effective Sparse Matrix Representation for the GPU Architectures

B. Neelima, Prakash S. Raghavendra

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 525 M, Optimization, Sparse matrix

May 25, 2012 by hgpu

Accelerating In-Memory Graph Database traversal using GPGPUS

Ashwin Raghav Mohan Ganesh

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

May 25, 2012 by hgpu

Parallel simulation of mixed-abstraction SystemC models on GPUs and multicore CPUs

Rohit Sinha, Aayush Prakash, Hiren D. Patel

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Tags: Computer science, CUDA, nVidia, Tesla C2075

May 25, 2012 by hgpu

Java on CUDA architecture

Jan Strnad

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Tags: Computer science, CUDA, Image processing, Java, Linear Algebra, Matrix multiplication, nVidia, Thesis

May 24, 2012 by hgpu

Sparse direct solvers with accelerators over DAG runtimes

Xavier Lacoste, Pierre Ramet, Mathieu Faverge, Yamazaki Ichitaro, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse direct solvers, Tesla T20

May 24, 2012 by hgpu

Compiler optimizations for directive-based programming for accelerators

Matias Piispanen

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, OpenCL, Optimization

May 24, 2012 by hgpu

GMProf: A Low-Overhead, Fine-Grained Profiling Approach for GPU Programs

Mai Zheng, Vignesh T. Ravi, Wenjing Ma, Feng Qin, Gagan Agrawal

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Tags: Computer science, CUDA, nVidia, Optimization, Performance, Tesla C1060

May 23, 2012 by hgpu

Adaptive fast multipole methods on the GPU

Anders Goude, Stefan Engblom

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Tags: Computer science, CUDA, Data Structures and Algorithms, Fast multipole method, nVidia, Package, Tesla C2075

May 23, 2012 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

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

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high performance computing on graphics processing units: hgpu.org

Programming

Fast and accurate digital signal processing realized with GPGPU technology

Parallelization of the Local Threshold and Boolean Function Based Edge Detection Algorithm Using CUDA

Using Compute Unified Device Architecture (CUDA) in Parallelizing Different Digital Image Processing Techniques

On the Simulations of Evolution-Communication P Systems with Energy without Antiport Rules for GPUs

Effective Sparse Matrix Representation for the GPU Architectures

Accelerating In-Memory Graph Database traversal using GPGPUS

Parallel simulation of mixed-abstraction SystemC models on GPUs and multicore CPUs

Java on CUDA architecture

Sparse direct solvers with accelerators over DAG runtimes

Compiler optimizations for directive-based programming for accelerators

GMProf: A Low-Overhead, Fine-Grained Profiling Approach for GPU Programs

Adaptive fast multipole methods on the GPU

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

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

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