CUDA enables parallel computation on GPUs
Image: OLCF at ORNL, CC BY 2.0, via Wikimedia Commons
CUDA
CUDA enables parallel computation on GPUs
The CUDA platform includes a variety of tools such as drivers, runtime kernels, compilers, libraries, and developer tools. These components work together to help programmers accelerate their applications by leveraging the parallel processing capabilities of GPUs.
Example
A CUDA kernel is a function that runs on thousands of GPU threads in parallel, enabling efficient execution of complex computations.
Understanding CUDA and its capabilities is crucial for developers working in fields that require high-performance computing, such as scientific research and simulations.
Related concepts
cooperative groups enable in CUDA: flexible thread synchronization patterns
Cooperative groups enable flexible thread synchronization patterns in CUDA
Parallel Thread Execution
PTX is an intermediate GPU instruction set used in Nvidia's CUDA
Thread block (CUDA programming)
Thread blocks can contain up to 1024 threads as of March 2010
tensor cores are
Tensor cores are specialized hardware for matrix multiply-accumulate on GPU
Dynamic random-access memory
DRAM requires periodic refreshing to maintain data integrity
kernel fusion reduces memory bandwidth bottleneck
Kernel fusion reduces memory bandwidth bottleneck by combining multiple operations into a single kernel, minimizing data transfers
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