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s MULTI
PROCESS SERVICE (MPS)
2
MOTIVATING USE CASE
Given a fixed amount of work to do, divided evenly among N MPI ranks: -What is the optimal value of N? -How many GPUs should we distribute these N ranks across? __global__ void kernel (double* x, int N) { int i = threadIdx.x + blockIdx.x * blockDim.x; if (i < N) { x[i] = 2 * x[i]; 3
BASE CASE: 1 RANK
Run with N = 10243
4
GPU COMPUTE MODES
NVIDIA GPUs have several compute modes
Default: multiple processes can run at one time
Exclusive Process: only one process can run at one time
Prohibited: no processes can run
Controllable with nvidia-smi --compute-mode; generallyneeds elevated privileges (so e.g.bsub-alloc_flagsgpudefaulton Summit) 5
SIMPLE OVERSUBSCRIPTION
The most common oversubscription case uses default mode
We simply target the same GPU with N ranks
$ jsrun -n 1 -a
-g 1 c ./test 1073741824 1 1.02 1.04 1.06 1.08 1.1 1.12 1.14 0510152025
Relative Runtime
Number of Ranks
6 OVERSUBSCRIPTION: 4 RANKS
Run with N = 10243
7 SIMPLE OVERSUBSCRIPTION
Each rank operates fully independently of all
other ranks Individual processes operate in time slices
A performance penalty is paid for switching
between time slices 8 ASIDE: CUDA CONTEXTS
Every process creates its own CUDA context
The context is a stateful object required to run CUDA Automatically created for you when using the CUDA runtime API On V100, the size is ~300 MB + your GPU code size
This limits the number of ranks we can fit on the GPU regardless of application data Context size is partially controlled by cudaLimitStackSize(more on that later) 9 MULTI-PROCESS TIMESLICING
ABC GPU A CPU Processes
GPU Interrupt
Timeslice1
10 MULTI-PROCESS TIMESLICING
ABC GPU A ABC GPU B CPU Processes
GPU Interrupt
Timeslice2
11 MULTI-PROCESS TIMESLICING
ABC GPU A ABC GPU B ABC GPUC CPU Processes
GPU InterruptTimeslice3
12 MULTI-PROCESS TIMESLICING
ABC GPU A Timeslice1
ABC GPU B Timeslice2
ABC GPUC Timeslice3
Full process isolation, peak throughput optimized for each process 13 WHEN DOES OVERSUBSCRIPTION HELP?
Perhaps a smaller case where launch latency is relevant? (N = 106) 14 WHEN DOES OVERSUBSCRIPTION HELP?
15 OVERSUBSCRIPTION CONCLUSIONS
No free lunch theorem applies: if GPU is fully utilized, cannot get faster answers But with GPU-only workloads, this rarely works out just right to be beneficialquotesdbs_dbs2.pdfusesText_2
Advanced Asynchronous Modulation Application Note - MPS