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Automatic Data Layout Generation and Kernel Mapping for CPU+GPU Architectures

Deepak Majeti, Kuldeep S. Meel, Rajkishore Barik, Vivek Sarkar

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Tags: Algorithms, Compilers, Computer science, Heterogeneous systems, nVidia, OpenCL, Programming techniques, Tesla M2050

March 10, 2016 by hgpu

Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit

David S. Lawrie

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Tags: Algorithms, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 780 M, nVidia GeForce GTX 980

March 10, 2016 by hgpu

Pragma Directed Shared Memory Centric Optimizations on GPUs

Jing Li, Lei Liu, Yuan Wu, Xiang-Hua Liu, Yi Gao, Xiao-Bing Feng, Cheng-Yong Wu

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Tags: ATI, ATI Radeon HD 7850, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, OpenACC, OpenCL, Tesla C2050

March 10, 2016 by hgpu

Study and evaluation of an Irregular Graph Algorithm on Multicore and GPU Processor Architectures

Varun Nagpal

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Performance, Tesla C1060, Thesis

March 10, 2016 by hgpu

Testing fine-grained parallelism for the ADMM on a factor-graph

Ning Hao, AmirReza Oghbaee, Mohammad Rostami, Nate Derbinsky, Jose Bento

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Source codes

Tags: Algorithms, Computer science, CUDA, Mathematical Software, nVidia, OpenMP, Package, Tesla K40

March 10, 2016 by hgpu

D-face: Parallel Implementation of CNN Based Face Classifier using Drone Data On K40 & Jetson TK1

Harish, Ayyappa, Santosh, P K Baruah, Kaliuday Balleda, Sairam K M Menon

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia Jetson TK1, Tesla K40

March 8, 2016 by hgpu

Enhancing productivity and performance portability of OpenCL applications on heterogeneous systems using runtime optimizations

Thibaut Lutz

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Tags: Computer science, nVidia, nVidia GeForce GTX 780, OpenCL, Performance, performance portability, PTX, Thesis

March 8, 2016 by hgpu

Compiler and runtime techniques for bulk-synchronous programming models on CPU architectures

Hee-Seok Kim

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Tags: Compilers, Computer science, OpenCL, performance portability, Thesis

March 8, 2016 by hgpu

A Novel Mapping of Arbitrary Precision Integer Operations to the GPU

Matthias Gusenbauer

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Tags: Computer science, nVidia, nVidia GeForce GTX Titan X, OpenCL, Thesis

March 8, 2016 by hgpu

Topology optimization design of 3D electrothermomechanical actuators by using GPU as a co-processor

Francisco Javier Ramírez-Gil, Emilío Carlos Nelli Silva, Wilfredo Montealegre-Rubio

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Tags: CUDA

March 7, 2016 by fjramireg

Performance Analysis of kNN on large datasets using CUDA & Pthreads

Sriram Kankatala

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Tags: Computer science, CUDA, Data mining, Databases, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX 550 Ti, Pattern recognition, Pthreads, Thesis

March 5, 2016 by hgpu

Fast LZW compression using a GPU

Shunji Funasaka, Koji Nakano, Yasuaki Ito

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Tags: Algorithms, Compression, CUDA, Image processing, nVidia, nVidia GeForce GTX 980

March 5, 2016 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

Automatic Data Layout Generation and Kernel Mapping for CPU+GPU Architectures

Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit

Pragma Directed Shared Memory Centric Optimizations on GPUs

Study and evaluation of an Irregular Graph Algorithm on Multicore and GPU Processor Architectures

Testing fine-grained parallelism for the ADMM on a factor-graph

D-face: Parallel Implementation of CNN Based Face Classifier using Drone Data On K40 & Jetson TK1

Enhancing productivity and performance portability of OpenCL applications on heterogeneous systems using runtime optimizations

Compiler and runtime techniques for bulk-synchronous programming models on CPU architectures

A Novel Mapping of Arbitrary Precision Integer Operations to the GPU

Topology optimization design of 3D electrothermomechanical actuators by using GPU as a co-processor

Performance Analysis of kNN on large datasets using CUDA & Pthreads

Fast LZW compression using a 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

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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