hgpu.org » Apple M2 Pro
Dahua Feng, Zhiming Xu, Rongxiang Wang, Felix Xiaozhu Lin
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
Recent source codes
* * *
Most viewed papers (last 30 days)
- CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe
- Revealing NVIDIA Closed-Source Driver Command Streams for CPU-GPU Runtime Behavior Insight
- MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU
- Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs
- Agentic Code Optimization via Compiler-LLM Cooperation
- FACT: Compositional Kernel Synthesis with a Three-Stage Agentic Workflow
- DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs
- DVM: Real-Time Kernel Generation for Dynamic AI Models
- ARGUS: Agentic GPU Optimization Guided by Data-Flow Invariants
- Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization
* * *



