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Places205-VGGNet Models for Scene Recognition

Limin Wang, Sheng Guo, Weilin Huang, Yu Qiao

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package

August 10, 2015 by hgpu

Practical Algorithms for Finding Extremal Sets

Martin Marinov, Nicholas Nash, David Gregg

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 580, Package

August 10, 2015 by hgpu

CRINK: Automatic CUDA code generation for affine C programs

Akanksha Singh

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Tags: Code generation, Computer science, CUDA, nVidia, Tesla C1060, Thesis

August 10, 2015 by hgpu

Towards Distortion-Predictable Embedding of Neural Networks

Axel Angel

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Thesis

August 7, 2015 by hgpu

Modern Platform for Parallel Algorithms Testing: Java on Intel Xeon Phi

Artur Malinowski

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Tags: Algorithms, Computer science, Intel Xeon Phi, Java

August 7, 2015 by hgpu

DenseCut: Densely Connected CRFs for Realtime GrabCut

M. M. Cheng, V. A. Prisacariu, S. Zheng, P. H. S. Torr, C. Rother

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Tags: Computer vision, CUDA, Image processing, nVidia, Package, Tesla K40

August 7, 2015 by hgpu

Optimising Reconfigurable Systems for Real-time Applications

Thomas Chun Pong Chau

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Tags: Algorithms, Computer science, CUDA, FPGA, Machine learning, Mixed precision, nVidia, Package, Tesla C2070, Thesis

August 7, 2015 by hgpu

Behavioral Spherical Harmonics for Long-Range Agents’ Interaction

Biagio Cosenza

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 960, OpenCL, OpenGL

August 7, 2015 by hgpu

A Survey Of Techniques for Architecting DRAM Caches

Sparsh Mittal, Jeffrey Vetter

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Tags: 3D DRAM, bandwidth, cpu, die-stacking, GPU, Memory, survey

August 7, 2015 by sparsh0mittal

Real-Time Pedestrian Detection With Deep Networks Cascades

Anelia Angelova, Alex Krizhevsky, Vincent Vanhoucke, Abhijit Ogale, Dave Ferguson

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Tags: Algorithms, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla K20

August 6, 2015 by hgpu

Exploiting two-level parallelism by aggregating computing resources in task-based applications over accelerator-based machines

Terry Cojean, Abdou Guermouche, Andra Hugo, Raymond Namyst, Pierre-Andre Wacrenier

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Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, OpenMP, Task scheduling, Tesla K40

August 5, 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

Places205-VGGNet Models for Scene Recognition

Practical Algorithms for Finding Extremal Sets

CRINK: Automatic CUDA code generation for affine C programs

Towards Distortion-Predictable Embedding of Neural Networks

Modern Platform for Parallel Algorithms Testing: Java on Intel Xeon Phi

DenseCut: Densely Connected CRFs for Realtime GrabCut

Optimising Reconfigurable Systems for Real-time Applications

Behavioral Spherical Harmonics for Long-Range Agents’ Interaction

A Survey Of Techniques for Architecting DRAM Caches

Real-Time Pedestrian Detection With Deep Networks Cascades

Exploiting two-level parallelism by aggregating computing resources in task-based applications over accelerator-based machines

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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