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hgpu.org » nVidia GeForce GTX Titan X

Performance of GTX Titan X GPUs and Code Optimization

Hwancheol Jeong, Sangbaek Lee, Weonjong Lee, Jeonghwan Pak, Jangho Kim, Juhyun Chung
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November 4, 2015 by hgpu

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing

Linnan Wang, Wei Wu, Jianxiong Xiao, Yi Yang
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October 22, 2015 by hgpu

Parallel Execution of the ASP Computation – an Investigation on GPUs

Agostino Dovier, Andrea Formisano, Enrico Pontelli, Flavio Vella
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September 5, 2015 by hgpu

Convolutional Neural Network for Sentence Classification

Yahui Chen
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September 5, 2015 by hgpu

Places205-VGGNet Models for Scene Recognition

Limin Wang, Sheng Guo, Weilin Huang, Yu Qiao
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August 10, 2015 by hgpu

Semantic Pose using Deep Networks Trained on Synthetic RGB-D

Jeremie Papon, Markus Schoeler
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August 5, 2015 by hgpu

Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation

Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, Juergen Schmidhuber
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June 26, 2015 by hgpu

Recent source codes

IntelliKit: Agent-first tooling for AMD hardware

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs
DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs
CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

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
Agentic Code Optimization via Compiler-LLM Cooperation

Agentic Code Optimization via Compiler-LLM Cooperation

Agentic Code Optimization via Compiler-LLM Cooperation
MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU
Device Virtual Machine (DVM)

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: 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

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context
LLM.Q: Quantized LLM training in pure CUDA/C++

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

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