Can CUDA use shared memory?
Shared memory is a powerful feature for writing well optimized CUDA code. Access to shared memory is much faster than global memory access because it is located on chip.
How are CUDA threads organized?
In CUDA, they are organized in a two-level hierarchy: a grid comprises blocks, and each block comprises threads. For all threads in a block, the block index is the same. The block index parameter can be accessed using the blockIdx variable inside a kernel.
What makes a CUDA code runs in parallel?
CUDA is a parallel computing platform and programming model developed by Nvidia for general computing on its own GPUs (graphics processing units). CUDA enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
What is the correct order of CUDA program processing?
In a typical CUDA program, data are first send from main memory to the GPU memory, then the CPU sends instructions to the GPU, then the GPU schedules and executes the kernel on the available parallel hardware, and finally results are copied back from the GPU memory to the CPU memory. …
What is constant memory in CUDA?
The constant memory in CUDA is a dedicated memory space of 65536 bytes. It is dedicated because it has some special features like cache and broadcasting. The constant memory space resides in device memory and is cached in the constant cache mentioned in Compute Capability 1.
What memory system is used in CUDA?
CUDA also uses an abstract memory type called local memory. Local memory is not a separate memory system per se but rather a memory location used to hold spilled registers. Register spilling occurs when a thread block requires more register storage than is available on an SM.
Is shared memory faster?
Shared memory is the fastest form of interprocess communication. The main advantage of shared memory is that the copying of message data is eliminated. The usual mechanism for synchronizing shared memory access is semaphores.