What is latency hiding?

Latency hiding is a strategy to mask long-latency operations by running many of them concurrently .

Performant GPU programs hide latency by interleaving the execution of many threads . This allows programs to maintain high throughput despite long instruction latencies. When one warp stalls on a slow memory operation, the GPU immediately switches to execute instructions from another eligible warp .

This keeps all execution units busy concurrently. While one warp uses Tensor Cores for matrix multiplication, another might execute arithmetic on CUDA Cores (say, quantizing or dequantizing matrix multiplicands ), and a third could be fetching data through the load/store units .

Concretely, consider the following simple instruction sequence in Streaming Assembler .

LDG.E.SYS R1, [R0]        // memory load, 400 cycles
IMUL R2, R1, 0xBEEF       // integer multiply, 6 cycles
IADD R4, R2, 0xAFFE       // integer add, 4 cycles
IMUL R6, R4, 0x1337       // integer multiply, 6 cycles

Executed sequentially, this would take 416 cycles to complete. We can hide this latency by operating concurrently. If we assume we can issue one instruction every cycle, then, by Little's Law , if we run 416 concurrent threads , we can still finish the sequence once per cycle (on average), hiding the latency of memory from consumers of the data in R6.

Note that threads are not the unit of instruction issuance, warps are. Each warp contains 32 threads , and so our fragment requires 416 ÷ 32 = 13 warps . When successfully hiding latency, the GPU's scheduling system maintains this many warps in flight, switching between them whenever one stalls, ensuring the execution units never idle while waiting for slow operations to complete.

For a deep dive into latency hiding on pre-Tensor Core GPUs, see Vasily Volkov's PhD thesis .