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Performance Analysis of CUDA and OpenCL By Implementation of Cryptographic Algorithms

Manas Mahapatra

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 525 M, OpenCL, Performance, Security, Thesis

November 23, 2016 by hgpu

Parallel local search on GPU and CPU with OpenCL

Omar Abdelkafi, Khalil Chebil, Mahdi Khemakhem

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Tags: Algorithms, Computer science, Metaheuristics, nVidia, nVidia GeForce GT 525 M, OpenCL, Optimization, Search

April 27, 2015 by hgpu

VDBSCAN+: Performance Optimization Based on GPU Parallelism

Carlos Roberto Valencio, Guilherme Priolli Daniel, Camila Alves de Medeiros, Adriano Mauro Cansian

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Tags: Algorithms, Computer science, CUDA, Data mining, Databases, nVidia, nVidia GeForce GT 525 M

October 18, 2013 by hgpu

Parallel Implementation of Dynamic Programming Algorithm Using Graphics Processing Unit

M. Khurram Rehman, M. Umer Sarwar, M. Ramzan Talib, M. Salman Mansoor, M. Bilal Sarwar

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Tags: Algorithms, Bioinformatics, Biology, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GT 525 M, nVidia GeForce GT 610 M, Sequence alignment

April 6, 2013 by hgpu

Distributed OpenCL Distributing OpenCL Platform on Network Scale

Baris Eskikaya, D Turgay Altilar

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Tags: ATI, ATI Radeon HD 6370 M, Computer science, Distributed computing, GPU cluster, nVidia, nVidia GeForce GT 240 M, nVidia GeForce GT 520 M, nVidia GeForce GT 525 M, nVidia GeForce GTX 540 M, nVidia GeForce GTX 555 M, OpenCL

July 15, 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

ATTUNE: Agentic Throughput Tuning for LLM Training

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

Brian2 Atlas

A Unified Intermediate Representation and Execution Architecture for Heterogeneous and Distributed Neural Simulation

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelLens: Deploying Triton Kernels to C++ Inference Engines

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

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high performance computing on graphics processing units: hgpu.org

Performance Analysis of CUDA and OpenCL By Implementation of Cryptographic Algorithms

Parallel local search on GPU and CPU with OpenCL

VDBSCAN+: Performance Optimization Based on GPU Parallelism

Parallel Implementation of Dynamic Programming Algorithm Using Graphics Processing Unit

Distributed OpenCL Distributing OpenCL Platform on Network Scale

Effective Sparse Matrix Representation for the GPU Architectures

Recent source codes

ATTUNE: Agentic Throughput Tuning for LLM Training

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

Brian2 Atlas

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

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