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Profiling Apple Silicon Performance for ML Training

Dahua Feng, Zhiming Xu, Rongxiang Wang, Felix Xiaozhu Lin

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Tags: AI, Apple M2 Max, Apple M2 Pro, Apple M2 Ultra, Computer science, CUDA, Linear Algebra, LLM, Machine learning, nVidia, nVidia GeForce RTX 4090, nVidia GeFroce RTX 2080 Ti, nVidia Quadro RTX 4000, nVidia RTX A6000, Performance, PyTorch

February 3, 2025 by hgpu

CUDAnalyst (CUDA + Analyst)

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

CodegenBench

CodegenBench: Can LLMs Write Efficient Code Across Architectures?

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

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 Multi-level Tiling Compiler for Parallel Tensor Programs

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

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

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

Agentic Code Optimization via Compiler-LLM Cooperation

Agentic Code Optimization via Compiler-LLM Cooperation

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