2 repositorios
Low-level interfaces for coordinating GPU processing and memory allocation via the CUDA driver.
Distinct from CUDA Profiling Tool APIs: None of the candidates cover the specific host-side CUDA Driver API; they focus on profiling, web APIs, or IoT hardware.
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Gorgonia is a Go library that provides an automatic differentiation engine and a computation graph framework for building and training neural networks. It functions as a CUDA-accelerated tensor library and a SIMD-optimized math library, enabling machine learning workflows entirely within the Go ecosystem. The library distinguishes itself through a dual-backend architecture that dispatches neural network operations to either a GPU or CPU depending on CUDA availability at runtime. It constructs differentiable directed acyclic graphs of tensor operations, supports reverse-mode automatic gradient
Manages GPU memory and kernel execution directly through the low-level CUDA Driver API from Go code.
cuda-python provides low-level Python bindings for the CUDA Driver and Runtime APIs. It serves as a programmatic wrapper for controlling device memory, managing hardware toolchains, and orchestrating execution graphs on NVIDIA GPUs, allowing for the compilation and launching of parallel kernels directly from Python. The project enables the development of SIMT kernels and the execution of mathematical algorithms on device memory. It integrates pre-compiled bytecode as custom operators and interfaces with accelerated device libraries to access low-level hardware functions without leaving the la
Provides a programmatic interface to CUDA driver and compiler tools for coordinating hardware processing and memory.