NVIDIA has released CUDA Toolkit 12.8, introducing several new features, improvements, and updates aimed at optimizing GPU performance and accelerating software development. This release enhances tools for profiling, debugging, and computation while also marking a transition away from older architectures.
Nsight Compute has received an improved range profiling feature, now including source-level metrics and guided analysis rules evaluation. These enhancements provide developers with deeper insights into performance bottlenecks and optimization strategies.
Compute Sanitizer now supports Python call stacks, making it easier to debug Python applications using CUDA. Additionally, new Tensor Core MMA guardrails have been added to ensure robustness in computations on the Blackwell architecture.
Math Libraries have been updated, including improvements to cuBLAS, cuSOLVER, nvJPEG, and NPP libraries. These updates support new architectures and introduce features such as microscaled 4-bit and 8-bit floating-point mixed-precision tensor core-accelerated matrix multiplication, further optimizing performance for AI and HPC workloads.
A new CUDA API function, cudaStreamGetDevice, has been introduced, allowing developers to retrieve the device associated with a CUDA stream. This provides better control over stream management in multi-GPU environments.
Compiler Updates
CUDA Toolkit 12.8 expands compiler support with GCC 14 as a host-side compiler. Additionally, for Blackwell architecture, the default high-level optimizer is now based on LLVM 18, improving code generation and efficiency.
Furthermore, nvdisasm now supports emitting JSON-formatted SASS disassembly, enhancing tooling for low-level debugging and analysis.
Accelerated Python Enhancements
CUDA Toolkit 12.8 includes significant updates for Python developers. An early prototype of a new idiomatic object model called cuda.core has been introduced, improving usability and integration with Python applications.
CUDA bindings have been moved to a dedicated submodule, cuda.bindings, offering better organization and maintainability. Additionally, early prototypes of parallel and cooperative algorithms using CCCL (CUDA Cooperative Computing Library) have been introduced.
A new version of CuPy has been released, incorporating Blackwell patches validated for general availability, further optimizing performance for Python-based numerical computing.
Feature-Complete Architectures
CUDA Toolkit 12.8 marks a transition in supported architectures. NVIDIA has announced that Maxwell, Pascal, and Volta architectures are now considered feature-complete. Future CUDA releases will not introduce new features for these architectures, and offline compilation support for them will be removed in the next major CUDA Toolkit release. This shift allows NVIDIA to focus on enhancing support for newer architectures.
This release offers full feature support for the NVIDIA Blackwell architecture, ensuring compatibility with the latest hardware advancements. Developers working with C++ and Python can now leverage enhanced support for accelerated libraries, compilers, and developer tools, enabling greater performance and efficiency.
CUDA Toolkit 12.8 delivers significant enhancements across profiling tools, debugging utilities, and computational libraries while shifting focus to modern architectures. By deprecating support for older GPUs, NVIDIA aims to optimize resources for cutting-edge developments in AI, HPC, and data science. Developers leveraging CUDA will benefit from these updates, ensuring continued innovation in GPU-accelerated computing.
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