CUDA: How many concurrent threads in total?

Question

I have a GeForce GTX 580, and I want to make a statement about the total number of threads that can (ideally) actually be run in parallel, to compare with 2 or 4 multi-core

Answer 1

I realize this is a bit late but I figured I'd help out anyway. From page 10 the CUDA Fermi architecture whitepaper:

Each SM features two warp schedulers and two instruction dispatch units, allowing two warps to be issued and executed concurrently.

To me this means that each SM can have 2*32=64 threads running concurrently. I don't know if that means that the GPU can have a total of 16*64=1024 threads running concurrently.

Answer 2

There are certain traps that you can fall into by doing that comparison to 2 or 4-core CPUs:

• The number of concurrent threads does not match the number of threads that actually run in parallel. Of course you can launch 24576 threads concurrently on GTX 580 but the optimal value is in most cases lower.

• A 2 or 4-core CPU can have arbitrary many concurrent threads! Similarly as with GPU, from some point adding more threads won't help, or even it may slow down.

• A "CUDA core" is a single scalar processing unit, while CPU core is usually a bigger thing, containing for example a 4-wide SIMD unit. To compare apples-to-apples, you should multiply the number of advertised CPU cores by 4 to match what NVIDIA calls a core.

• CPU supports hyperthreading, which allows a single core to process 2 threads concurrently in a light way. Because of that, an operating system may actually see 2 times more "logical cores" than the hardware cores.

To sum it up: For a fair comparison, your 4-core CPU can actually run 32 "scalar threads" concurrently, because of SIMD and hyperthreading.

Answer 3

The GTX 580 can have 16 * 48 concurrent warps (32 threads each) running at a time. That is 16 multiprocessors (SMs) * 48 resident warps per SM * 32 threads per warp = 24,576 threads.

Don't confuse concurrency and throughput. The number above is the maximum number of threads whose resources can be stored on-chip simultaneously -- the number that can be resident. In CUDA terms we also call this maximum occupancy. The hardware switches between warps constantly to help cover or "hide" the (large) latency of memory accesses as well as the (small) latency of arithmetic pipelines.

While each SM can have 48 resident warps, it can only issue instructions from a small number (on average between 1 and 2 for GTX 580, but it depends on the program instruction mix) of warps at each clock cycle.

So you are probably better off comparing throughput, which is determined by the available execution units and how the hardware is capable of performing multi-issue. On GTX580, there are 512 FMA execution units, but also integer units, special function units, memory instruction units, etc, which can be dual-issued to (i.e. issue independent instructions from 2 warps simultaneously) in various combinations.

Taking into account all of the above is too difficult, though, so most people compare on two metrics:

1. Peak GFLOP/s (which for GTX 580 is 512 FMA units * 2 flops per FMA * 1544e6 cycles/second = 1581.1 GFLOP/s (single precision))
2. Measured throughput on the application you are interested in.

The most important comparison is always measured wall-clock time on a real application.