开源CUDA项目起死回生，支持非英伟达芯片，濒临倒闭时神秘机构出手援助

Core Viewpoint - The open-source project ZLUDA, which enables non-NVIDIA chips to run CUDA, has been revived after facing near bankruptcy due to the withdrawal of AMD's support. A mysterious organization has stepped in to provide assistance, allowing the project to continue its development and support for large model workloads [1][2][12]. Historical Development - ZLUDA was initiated by Andrzej Janik, who previously worked at Intel, aiming to allow CUDA programs to run on non-NVIDIA platforms [4][5]. - Initially, ZLUDA was taken over by Intel as an internal project to run CUDA programs on Intel GPUs, but it was soon terminated [6][9]. - In 2022, ZLUDA received support from AMD but was again halted in February 2024 after NVIDIA released CUDA 11.6, which restricted reverse engineering on non-NVIDIA platforms [10][11][12]. Recent Developments - In October 2024, Janik announced that ZLUDA had received support from a mysterious organization, focusing on machine learning and aiming to restore the project to its previous state by Q3 2025 [13][15]. - The project has added a new full-time developer, Violet, who has made significant improvements, particularly in supporting large language model workloads [17]. Technical Progress - ZLUDA is working on enabling 32-bit PhysX support, with community contributors identifying and fixing errors that may also affect 64-bit CUDA functionality [19]. - A test project named llm.c is being developed to run the GPT-2 model using CUDA, marking ZLUDA's first attempt to handle both standard CUDA functions and specialized libraries like cuBLAS [20][22]. - The team has made progress in supporting 16 out of 44 required functions for the test program, indicating a step closer to full functionality [25]. Accuracy and Logging Improvements - ZLUDA aims to run standard CUDA programs on non-NVIDIA GPUs while matching NVIDIA hardware as closely as possible. Recent efforts have focused on improving accuracy by implementing PTX "scan" tests to ensure correct results across all inputs [26][28]. - The logging system has been significantly upgraded to track previously invisible activities and internal behaviors, which is crucial for running any CUDA-based software on ZLUDA [31][33]. Runtime Compiler Compatibility - ZLUDA has addressed issues related to the dynamic compilation of device code necessary for compatibility with modern GPU frameworks. Recent changes in the ROCm/HIP ecosystem have led to unexpected errors, but the ZLUDA team has resolved these problems [34][36][38].