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Synthesizing Software from a ForSyDe Model Targeting GPGPUs

Gabriel Hjort Blindell

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia Quadro FX 600, nVidia Quadro NVS 290, Software Engineering, Thesis

March 6, 2012 by hgpu

Reuse and Refactoring of GPU Kernels to Design Complex Applications

Santonu Sarkar, Sayantan Mitra, Ashok Srinivasan

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Tags: Computer science, CUDA, FFT, Molecular dynamics, nVidia, nVidia GeForce GTX 480, Optimization, Software Engineering, Tesla S1050

February 24, 2012 by hgpu

Automatic SIMD Code Generation

Roland Leissa

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Tags: Algorithms, Code generation, Computer science, Optimization, Performance, Software Engineering, Thesis

January 4, 2012 by hgpu

Minimising Testing in Genetic Programming

W. B. Langdon

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Genetic programming, nVidia, Software Engineering

December 15, 2011 by hgpu

GPU-Based Image Processing Use Cases: A High-Level Approach

Volkmar Wieser, Clemens Grelck, Holger Schoner, Peter Haslinger, Karoly Bosa, Bernhard Moser

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Tags: Benchmarking, CUDA, Image processing, nVidia, nVidia GeForce GT 425 M, Software Engineering, Virtualization

November 21, 2011 by hgpu

MELT-a Translated Domain Specific Language Embedded in the GCC Compiler

Basile Starynkevitch

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Tags: Compilers, Computer science, OpenCL, Optimization, Programming Languages, Software Engineering

November 7, 2011 by hgpu

An Adaptive Framework for Managing Heterogeneous Many-Core Clusters

Muhammad Mustafa Rafique

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, MapReduce, nVidia, Prefetch, Software Engineering, Tesla C2050, Thesis

October 28, 2011 by hgpu

Parallelization of Single Threaded Applications using OpenMP and CUDA/C

John Burns

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Tags: Computer science, CUDA, Databases, nVidia, OpenMP, Software Engineering

October 27, 2011 by hgpu

CUDA Application Design and Development

Rob Farber

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Tags: Book, Computer science, CUDA, nVidia, Review, Software Engineering

October 22, 2011 by hgpu

Numerical Accuracy Analysis Based on the Discrete Stochastic Arithmetic on Multiprocessor Platforms

Christian Motzing

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Tags: Computer science, CUDA, Molecular dynamics, Numerical Analysis, nVidia, nVidia GeForce GTX 480, Review, Software Engineering, Thesis

October 17, 2011 by hgpu

Highly Scalable Multi Objective Test Suite Minimisation Using Graphics Cards

Shin Yoo, Mark Harman, Shmuel Ur

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Tags: Algorithms, ATI, ATI Radeon HD 4850, Benchmarking, Computer science, OpenCL, Optimization, Software Engineering

September 28, 2011 by hgpu

2PARMA: Parallel Paradigms and Run-time Management Techniques for Many-Core Architectures

C.Silvano, W.Fornaciari, S.Crespi Reghizzi, G.Agosta, G.Palermo, V.Zaccaria, P.Bellasi, F.Castro, S.Corbetta, A.Di Biagio, E.Speziale, M. Tartara, D.Siorpaes, H.Hubert, B.Stabernack, J.Brandenburg, M.Palkovic, P.Raghavan, C.Ykman-Couvreur, A.Bartzas, S.Xydis, D.Soudris, T.Kempf, G.Ascheid, R.Leupers, H.Meyr, J.Ansari, P.Mahonen, B.Vanthournout

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Tags: Compilers, Computer science, Design space exploration, OpenCL, Performance, Programming techniques, Software Engineering

September 28, 2011 by hgpu

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

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

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

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

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