CUDA performance improves when running more threads than there are cores

Question

Why does performance improve when I run more than 32 threads per block?

My graphics card has 480 CUDA Cores (15 MS * 32 SP).

Answer 1

Each SM has 1-4 warp schedulers (Tesla = 1, Fermi = 2, Kepler = 4). Each warp scheduler is responsible for executing a subset of the warps allocated to the SM. Each warp scheduler maintains a list of eligible warps. A warp is eligible if it can issue an instruction on the next cycle. A warp is not eligible if it is stalled on a data dependency, waiting to fetch and instruction, or the execution unit for the next instruction is busy. On each cycle each warp scheduler will pick a warp from the list of eligible warp and issue 1 or 2 instructions.

The more active warps per SM the larger the number of warps each warp scheduler will have to pick from on each cycle. In most cases, optimal performance is achieved when there is sufficient active warps per SM to have 1 eligible warp per warp scheduler per cycle. Increasing occupancy beyond this point does not increase performance and may decrease performance.

A typical target for active warps is 50-66% of the maximum warps for the SM. The ratio of warps to maximum warps supported by a launch configuration is called Theoretical Occupancy. The runtime ratio of active warps per cycle to maximum warps per cycle is Achieved Occupancy. For a GTX480 (CC 2.0 device) a good starting point when designing a kernel is 50-66% Theoretical Occupancy. CC 2.0 SM can have a maximum of 48 warps. A 50% occupancy means 24 warps or 768 threads per SM.

The CUDA Profiling Activity in Nsight Visual Studio Edition can show the theoretical occupancy, achieved occupancy, active warps per SM, eligible warps per SM, and stall reasons.

The CUDA Visual Profiler, nvprof, and the command line profiler can show theoretical occupancy, active warps, and achieved occupancy.

NOTE: The count of CUDA cores should only be used to compare cards of similar architectures, to calculate theoretical FLOPS, and to potentially compare differences between architectures. Do not use the count when designing algorithms.

Answer 2

Welcome to Stack Overflow. The reason is that CUDA cores are pipelined. On Fermi, the pipeline is around 20 clocks long. This means that to saturate the GPU, you may need up to 20 threads per core.

Answer 3

The primary reason is the memory latency hiding model of CUDA. Most modern CPU's use cache to hide the latency to main memory. This results in a large percentage of chip resources being devoted to cache. Most desktop and server processors have several megabytes of cache on the die, which actually accounts for most of the die space. In order to pack on more cores with the same energy usage and heat dissipation characteristics, CUDA-based chips instead devote their chip space to throwing on tons of CUDA cores (which are mostly just floating-point ALU's.) Since there is very little cache, they instead rely on having more threads ready to run while other threads are waiting on memory accesses to return in order to hide that latency. This gives the cores something productive to be working on while some warps are waiting on memory accesses. The more warps per SM, the more chance one of them will be runnable at any given time.

CUDA also has zero-cost thread switching in order to aid in this memory-latency-hiding scheme. A normal CPU will incur a large overhead to switch from execution of one thread to the next due to need to store all of the register values for the thread it is switching away from onto the stack and then loading all of the ones for the thread it is switching to. CUDA SM's just have tons and tons of registers, so each thread has its own set of physical registers assigned to it through the life of the thread. Since there is no need to store and load register values, each SM can execute threads from one warp on one clock cycle and execute threads from a different warp on the very next clock cycle.