dcuda: Distributed GPU Computing with Hardware Overlap

Transcription

1 dcuda: Distributed GPU Computing with Hardware Overlap Tobias Gysi, Jeremia Bär, Lukas Kuster, and Torsten Hoefler

2 GPU computing gained a lot of popularity in various application domains weather & climate machine learning molecular dynamics

3 GPU cluster programming using MPI and CUDA node 1 node 2 code device memory device memory // run compute kernel global void mykernel( ) { } PCI-Express PCI-Express host memory interconnect PCI-Express PCI-Express host memory // launch compute kernel mykernel<<<64,128>>>( ); // on-node data movement cudamemcpy( psize, &size, sizeof(int), cudamemcpydevicetohost); // inter-node data movement mpi_send( pdata, size, MPI_FLOAT, ); mpi_recv( pdata, size, MPI_FLOAT, );

4 time Disadvantages of the MPI-CUDA approach mykernel( ) { } device host complexity mykernel<<< >>>( ); cudamemcpy( ); device sync two programming models duplicated functionality copy sync mpi_send( ); mpi_recv( ); mykernel( ) { } cluster sync mykernel<<< >>>( ); performance encourages sequential execution low utilization of the costly hardware

5 time Achieve high resource utilization using oversubscription & hardware threads code thread 1 thread 2 thread 3 instruction pipeline %r0,%r1 %r0,%r1 %r0,%r0,3 %r0,%r1 st %r0,%r1 %r0,%r1 %r0,%r0,3 GPU cores use parallel slack to hide instruction pipeline latencies st %r0,%r1 %r0,%r0,3 %r0,%r0,3 st st %r0,%r1 st st st %r0,%r1 st st

6 time Use oversubscription & hardware threads to hide remote memory latencies code thread 1 thread 2 thread 3 instruction pipeline get get get %r0,%r0,3 get get get put get get get! get introduce put & get operations to access distributed memory %r0,%r0,3!!! %r0,%r0,3 %r0,%r0,3 put put

7 How much parallel slack is necessary to fully utilize the interconnect? Little s law concurrency = latency throughput device memory interconnect latency ~1µs 7µs bandwidth 200GB/s 6GB/s concurrency 200kB 42kB #threads ~12000 >> ~3000

8 dcuda (distributed CUDA) extends CUDA with MPI-3 RMA and notifications for (int i = 0; i < steps; ++i) { for (int idx = from; idx < to; idx += jstride) out[idx] = -4.0 * in[idx] + in[idx + 1] + in[idx - 1] + in[idx + jstride] + in[idx - jstride]; computation iterative stencil kernel thread specific idx } if (lsend) dcuda_put_notify(ctx, wout, rank - 1, len + jstride, jstride, &out[jstride], tag); if (rsend) dcuda_put_notify(ctx, wout, rank + 1, 0, jstride, &out[len], tag); dcuda_wait_notifications(ctx, wout, tag, lsend + rsend); swap(in, out); swap(win, wout); communication map ranks to blocks device-side put/get operations notifications for synchronization shared and distributed memory T. Gysi, J. Baer, TH: dcuda: Hardware Supported Overlap of Computation and Communication, SC16

9 rank 1 rank 2 rank 3 rank 4 time Advantages of the dcuda approach rank 1 stencil( ); sync stencil( ); device 1 rank 2 stencil( ); sync stencil( ); rank 3 stencil( ); sync stencil( ); device 2 rank 4 stencil( ); sync stencil( ); performance avoid device synchronization latency hiding at cluster scale complexity unified programming model one communication mechanism device 1 put put device 2 put T. Gysi, J. Baer, TH: dcuda: Hardware Supported Overlap of Computation and Communication, SC16

10 device-side host-side Implementation of the dcuda runtime system event handler block manager block manager block manager MPI/IB device-library device-library device-library GPU direct get( ); get( ); get( ); T. Gysi, J. Baer, TH: dcuda: Hardware Supported Overlap of Computation and Communication, SC16

11 GPUDirect provides the NIC with direct device memory access PCI-E IB PCI-E device NIC NIC device host GPUDirect host staging host idea avoid copy to host memory host-side control performance lower latency bandwidth penalty on Greina (2.7GB/s instead of 7.2GB/s)

12 System latencies of the IB-backend compared to the MPI-backend benchmarked on Greina (4 Broadwell nodes with 1x Tesla K80 per node) IB-backend MPI-backend same device (put & notify) [µs] peer device (put & notify) [µs] remote device (put & notify) [µs] same device (notify) [µs] peer device (notify) [µs] remote device (notify) [µs] T. Gysi, J. Baer, TH: dcuda: Hardware Supported Overlap of Computation and Communication, SC16

13 Overlap of a copy kernel with halo exchange communication benchmarked on Greina (8 Haswell nodes with 1x Tesla K80 per node) no overlap execution time [ms] compute & exchange halo exchange compute only # of copy iterations per exchange T. Gysi, J. Baer, TH: dcuda: Hardware Supported Overlap of Computation and Communication, SC16

14 execution time [ms] Weak scaling of IB-CUDA and dcuda for a particle siation benchmarked on Greina (4 Broadwell nodes with 1x Tesla K80 per node) 150 IB-CUDA dcuda IB-backend 0 communication number of GPUs

15 execution time [ms] Weak scaling of IB-CUDA and dcuda for a stencil program benchmarked on Greina (4 Broadwell nodes with 1x Tesla K80 per node) IB-CUDA IB-backend dcuda 50 communication number of GPUs

16 execution time [ms] Weak scaling of IB-CUDA and dcuda for sparse-matrix vector tiplication benchmarked on Greina (4 Broadwell nodes with 1x Tesla K80 per node) IB-CUDA IB-backend 50 dcuda 0 communication number of GPUs

17 execution time [ms] Weak scaling of IB-CUDA and dcuda for power iterations benchmarked on Greina (4 Broadwell nodes with 1x Tesla K80 per node) IB-CUDA dcuda IB-backend 0 communication number of GPUs

18 T. Gysi, J. Baer, TH: dcuda: Hardware Supported Overlap of Computation and Communication, SC16 Conclusions unified programming model for GPU clusters device-side remote memory access operations with notifications transparent support of shared and distributed memory extend the latency hiding technique of CUDA to the full cluster inter-node communication without device synchronization use oversubscription & hardware threads to hide remote memory latencies automatic overlap of computation and communication synthetic benchmarks demonstrate perfect overlap example applications demonstrate the applicability to real codes