SPEChpc™ 2021 Medium Result

Copyright 2021-2022 Standard Performance Evaluation Corporation

NVIDIA Corporation

Selene: NVIDIA DGX SuperPOD
(AMD EPYC 7742 2.25 GHz, Tesla A100-SXM-80 GB)

SPEChpc 2021_med_base = 44.70

SPEChpc 2021_med_peak = Not Run

hpc2021 License: 019 Test Date: Sep-2022
Test Sponsor: NVIDIA Corporation Hardware Availability: Jul-2020
Tested by: NVIDIA Corporation Software Availability: Mar-2022

Benchmark result graphs are available in the PDF report.

Results Table

Benchmark Base Peak
Model Ranks Thrds/Rnk Seconds Ratio Seconds Ratio Seconds Ratio Model Ranks Thrds/Rnk Seconds Ratio Seconds Ratio Seconds Ratio
SPEChpc 2021_med_base 44.70
SPEChpc 2021_med_peak Not Run
Results appear in the order in which they were run. Bold underlined text indicates a median measurement.
705.lbm_m ACC 1024 16 18.3 66.9 18.2 67.2 18.1 67.6
718.tealeaf_m ACC 1024 16 35.3 38.3 35.8 37.7 35.5 38.0
719.clvleaf_m ACC 1024 16 26.8 68.9 27.3 67.7 27.0 68.4
728.pot3d_m ACC 1024 16 63.8 29.0 63.6 29.1 65.2 28.4
734.hpgmgfv_m ACC 1024 16 66.3 15.1 66.6 15.0 66.3 15.1
735.weather_m ACC 1024 16 23.0 1040 23.8 1010 22.7 1060
Hardware Summary
Type of System: SMP
Compute Node: DGX A100
Interconnects: Multi-rail InfiniBand HDR fabric
DDN EXAScalar file system
Compute Nodes Used: 64
Total Chips: 128
Total Cores: 8192
Total Threads: 16384
Total Memory: 128 TB
Software Summary
Compiler: C/C++/Fortran: Version 22.3 of
NVIDIA HPC SDK for Linux
MPI Library: OpenMPI Version 4.1.2rc4
Other MPI Info: HPC-X Software Toolkit Version 2.10
Other Software: None
Base Parallel Model: ACC
Base Ranks Run: 1024
Base Threads Run: 16
Peak Parallel Models: Not Run

Node Description: DGX A100

Hardware
Number of nodes: 64
Uses of the node: compute
Vendor: NVIDIA Corporation
Model: NVIDIA DGX A100 System
CPU Name: AMD EPYC 7742
CPU(s) orderable: 2 chips
Chips enabled: 2
Cores enabled: 128
Cores per chip: 64
Threads per core: 2
CPU Characteristics: Turbo Boost up to 3400 MHz
CPU MHz: 2250
Primary Cache: 32 KB I + 32 KB D on chip per core
Secondary Cache: 512 KB I+D on chip per core
L3 Cache: 256 MB I+D on chip per chip
(16 MB shared / 4 cores)
Other Cache: None
Memory: 2 TB (32 x 64 GB 2Rx8 PC4-3200AA-R)
Disk Subsystem: OS: 2TB U.2 NVMe SSD drive
Internal Storage: 30TB (8x 3.84TB U.2 NVMe SSD
drives)
Other Hardware: None
Accel Count: 8
Accel Model: Tesla A100-SXM-80 GB
Accel Vendor: NVIDIA Corporation
Accel Type: GPU
Accel Connection: NVLINK 3.0, NVSWITCH 2.0 600 GB/s
Accel ECC enabled: Yes
Accel Description: See Notes
Adapter: NVIDIA ConnectX-6 MT28908
Number of Adapters: 8
Slot Type: PCIe Gen4
Data Rate: 200 Gb/s
Ports Used: 1
Interconnect Type: InfiniBand / Communication
Adapter: NVIDIA ConnectX-6 MT28908
Number of Adapters: 2
Slot Type: PCIe Gen4
Data Rate: 200 Gb/s
Ports Used: 2
Interconnect Type: InfiniBand / FileSystem
Software
Accelerator Driver: NVIDIA UNIX x86_64 Kernel Module 470.103.01
Adapter: NVIDIA ConnectX-6 MT28908
Adapter Driver: InfiniBand: 5.4-3.4.0.0
Adapter Firmware: InfiniBand: 20.32.1010
Adapter: NVIDIA ConnectX-6 MT28908
Adapter Driver: Ethernet: 5.4-3.4.0.0
Adapter Firmware: Ethernet: 20.32.1010
Operating System: Ubuntu 20.04
5.4.0-121-generic
Local File System: ext4
Shared File System: Lustre
System State: Multi-user, run level 3
Other Software: None

Interconnect Description: Multi-rail InfiniBand HDR fabric

Hardware
Vendor: NVIDIA
Model: N/A
Switch Model: NVIDIA Quantum QM8700
Number of Switches: 164
Number of Ports: 40
Data Rate: 200 GB/s per port
Firmware: MLNX-OS v3.10.2202
Topology: Full three-level fat-tree
Primary Use: Inter-process communication
Software

Interconnect Description: DDN EXAScalar file system

Hardware
Vendor: NVIDIA
Model: N/A
Switch Model: NVIDIA Quantum QM8700
Number of Switches: 26
Number of Ports: 40
Data Rate: 200 GB/s per port
Firmware: MLNX-OS v3.10.2202
Topology: Full three-level fat-tree
Primary Use: Global storage
Software

Compiler Invocation Notes

 Binaries built and run within a NVHPC SDK 22.3 CUDA 11.0 Ubuntu 20.04
  Container available from NVIDIA GPU Cloud (NGC):
   https://ngc.nvidia.com/catalog/containers/nvidia:nvhpc
   https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nvhpc/tags

Submit Notes

The config file option 'submit' was used.
 MPI startup command:
   srun command was used to start MPI jobs.

 Individual Ranks were bound to the NUMA nodes, GPUs and NICs using this "wrapper.GPU" bash-script for the case of 1 rank per GPU

   ln -s -f libnuma.so.1 /usr/lib/x86_64-linux-gnu/libnuma.so
   export LD_LIBRARY_PATH+=:/usr/lib/x86_64-linux-gnu
   export LD_RUN_PATH+=:/usr/lib/x86_64-linux-gnu
   declare -a NUMA_LIST
   declare -a  GPU_LIST
   declare -a  NIC_LIST
   NUMA_LIST=($NUMAS)
   GPU_LIST=($GPUS)
   NIC_LIST=($NICS)
   export UCX_NET_DEVICES=${NIC_LIST[$SLURM_LOCALID]}:1
   export OMPI_MCA_btl_openib_if_include=${NIC_LIST[$SLURM_LOCALID]}
   export CUDA_VISIBLE_DEVICES=${GPU_LIST[$SLURM_LOCALID]}
   numactl -l -N ${NUMA_LIST[$SLURM_LOCALID]} $*

 and this "wrapper.MPS" bash-script for the oversubscribed case.

   ln -s -f libnuma.so.1 /usr/lib/x86_64-linux-gnu/libnuma.so
   export LD_LIBRARY_PATH+=:/usr/lib/x86_64-linux-gnu
   export LD_RUN_PATH+=:/usr/lib/x86_64-linux-gnu
   declare -a NUMA_LIST
   declare -a  GPU_LIST
   declare -a  NIC_LIST
   NUMA_LIST=($NUMAS)
   GPU_LIST=($GPUS)
   NIC_LIST=($NICS)
   NUM_GPUS=${#GPU_LIST[@]}
   RANKS_PER_GPU=$((SLURM_NTASKS_PER_NODE / NUM_GPUS))
   GPU_LOCAL_RANK=$((SLURM_LOCALID / RANKS_PER_GPU))
   export UCX_NET_DEVICES=${NIC_LIST[$GPU_LOCAL_RANK]}:1
   export OMPI_MCA_btl_openib_if_include=${NIC_LIST[$GPU_LOCAL_RANK]}
   set +e
   nvidia-cuda-mps-control -d 1>&2
   set -e
   export CUDA_VISIBLE_DEVICES=${GPU_LIST[$GPU_LOCAL_RANK]}
   numactl -l -N ${NUMA_LIST[$GPU_LOCAL_RANK]} $*
   if [ $SLURM_LOCALID -eq 0 ]
   then
       echo 'quit' | nvidia-cuda-mps-control 1>&2
   fi

General Notes

Full system details documented here:
https://images.nvidia.com/aem-dam/Solutions/Data-Center/gated-resources/nvidia-dgx-superpod-a100.pdf

Environment variables set by runhpc before the start of the run:
SPEC_NO_RUNDIR_DEL = "on"

Platform Notes

 Detailed A100 Information from nvaccelinfo
 CUDA Driver Version:           11040
 NVRM version:                  NVIDIA UNIX x86_64 Kernel Module 470.7.01
 Device Number:                 0
 Device Name:                   NVIDIA A100-SXM-80 GB
 Device Revision Number:        8.0
 Global Memory Size:            85198045184
 Number of Multiprocessors:     108
 Concurrent Copy and Execution: Yes
 Total Constant Memory:         65536
 Total Shared Memory per Block: 49152
 Registers per Block:           65536
 Warp Size:                     32
 Maximum Threads per Block:     1024
 Maximum Block Dimensions:      1024, 1024, 64
 Maximum Grid Dimensions:       2147483647 x 65535 x 65535
 Maximum Memory Pitch:          2147483647B
 Texture Alignment:             512B
 Clock Rate:                    1410 MHz
 Execution Timeout:             No
 Integrated Device:             No
 Can Map Host Memory:           Yes
 Compute Mode:                  default
 Concurrent Kernels:            Yes
 ECC Enabled:                   Yes
 Memory Clock Rate:             1593 MHz
 Memory Bus Width:              5120 bits
 L2 Cache Size:                 41943040 bytes
 Max Threads Per SMP:           2048
 Async Engines:                 3
 Unified Addressing:            Yes
 Managed Memory:                Yes
 Concurrent Managed Memory:     Yes
 Preemption Supported:          Yes
 Cooperative Launch:            Yes
   Multi-Device:                Yes
 Default Target:                cc80

Compiler Version Notes

==============================================================================
 CC  705.lbm_m(base) 718.tealeaf_m(base) 734.hpgmgfv_m(base)
------------------------------------------------------------------------------
nvc 22.3-0 64-bit target on x86-64 Linux -tp zen2-64 
NVIDIA Compilers and Tools
Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
------------------------------------------------------------------------------

==============================================================================
 FC  719.clvleaf_m(base) 728.pot3d_m(base) 735.weather_m(base)
------------------------------------------------------------------------------
nvfortran 22.3-0 64-bit target on x86-64 Linux -tp zen2-64 
NVIDIA Compilers and Tools
Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
------------------------------------------------------------------------------

Base Compiler Invocation

C benchmarks:

 mpicc 

Fortran benchmarks:

 mpif90 

Base Portability Flags

705.lbm_m:  -DSPEC_OPENACC_NO_SELF 

Base Optimization Flags

C benchmarks:

 -fast   -DSPEC_ACCEL_AWARE_MPI   -acc=gpu   -gpu=cuda11.0   -gpu=cc80   -Mstack_arrays   -Mfprelaxed   -Mnouniform   -tp=zen2 

Fortran benchmarks:

 -DSPEC_ACCEL_AWARE_MPI   -fast   -acc=gpu   -gpu=cuda11.0   -gpu=cc80   -Mstack_arrays   -Mfprelaxed   -Mnouniform   -tp=zen2 

Base Other Flags

C benchmarks (except as noted below):

 -Ispecmpitime   -w 
734.hpgmgfv_m:  -Ispecmpitime    -w 

Fortran benchmarks (except as noted below):

 -w 
719.clvleaf_m:  -Ispecmpitime   -w 

The flags file that was used to format this result can be browsed at
http://www.spec.org/hpc2021/flags/nv2021_flags_v1.0.3.2022-11-03.html.

You can also download the XML flags source by saving the following link:
http://www.spec.org/hpc2021/flags/nv2021_flags_v1.0.3.2022-11-03.xml.