The idea is that the CPU spawns a thread per element, and the GPU then executes those threads. Not all of the thousands or millions of threads actually run in parallel, but many do. Specifically, an NVIDIA GPU contains several largely independent processors called "Streaming Multiprocessors" (SMs), each SM hosts several "cores", and each "core" runs a thread. For instance, Fermi has up to 16 SMs with 32 cores per SM – so up to 512 threads can run in parallel.
GPUs are designed specifically for graphics and thus are very restrictive in operations and programming. Due to their design, GPUs are only effective for problems that can be solved using stream processing and the hardware can only be used in certain ways.
Arithmetic intensity is defined as the number of operations performed per word of memory transferred. It is important for GPGPU applications to have high arithmetic intensity else the memory access latency will limit computational speedup. 22 »
Ideal GPGPU applications have large data sets, high parallelism, and minimal dependency between data elements.
Arithmetic intensity is defined as the number of operations performed per word of memory transferred. It is important for GPGPU applications to have high arithmetic intensity else the memory access latency will limit computational speedup. 22 »
Ideal GPGPU applications have large data sets, high parallelism, and minimal dependency between data elements.
