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Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung

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Tags: Artificial intelligence, Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce GTX Titan XP, nVidia Tesla GP100

November 11, 2018 by hgpu

NVIDIA Nemotron Parse 1.1

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

mt4g: Memory Topology 4 GPUs

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

A High-Throughput GPU Framework for Adaptive Lossless Compression of Floating-Point Data

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

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