Just in time for SC’19, Azure launched into Preview this week the new HBv2 Virtual Machines for High-Performance Computing (HPC).
These VMs feature a wealth of new technology, including:
AMD EPYC 7742 CPUs (Rome)
2.45 GHz Base clock / 3.3 GHz Boost clock
480 MB L3 cache, 480 GB RAM
340 GB/s of Memory Bandwidth
200 Gbps HDR InfiniBand (SRIOV) with Adaptive Routing
900 GB SSD (NVMeDirect)
Below are initial performance characterizations using a variety of configurations on both microbenchmarks as well as commonly used HPC applications for which the HB family of VMs is optimized for.
Microbenchmarks
MPI Latency
OSU Benchmarks (5.6.2) – osu_latency with MPI = HPC-X, Intel MPI, MVAPICH2, OpenMPI
Message Size (bytes)
HPC-X
Intel MPI
MVAPICH2
OpenMPI
0
1.62
1.95
1.85
1.61
1
1.62
1.95
1.9
1.61
2
1.61
1.95
1.9
1.61
4
1.62
1.95
1.9
1.61
8
1.61
1.96
1.9
1.61
16
1.62
1.96
1.93
1.62
32
1.77
1.97
1.94
1.77
64
1.83
2.03
2.08
1.82
128
1.9
2.09
2.29
1.9
256
2.44
2.65
2.78
2.44
512
2.53
2.71
2.84
2.53
1024
2.63
2.84
2.93
2.62
2048
2.92
3.09
3.13
2.92
4096
3.72
3.89
4.07
3.74
MPI Bandwidth (2 QP’s)
OSU Benchmarks (5.6.2) – osu_mbw_mr with ppn = 2 with MPI = HPC-X
#bytes
BW peak
BW average
4096
15920.65
15902.67
8192
23045.57
23036.88
16384
23270.14
23260.04
32768
23376.91
23372.9
65536
23423.49
23423.23
131072
23445.05
23443.6
262144
23463.94
23463.93
524288
23470.7
23470.55
1048576
23474.3
23474.08
2097152
23475.77
23475.73
4194304
23476.61
23476.61
8388608
23477.06
23477.05
Application Benchmarks
App: Siemens Star-CCM+
Version: 14.06.004
Model: LeMans 100M Coupled Solver
Configuration Details: 116 MPI ranks were run (4 ranks from each of 29 NUMA) in each HBv2 VM in order to leave nominal resources to run Linux background processes. In addition, Adaptive Routing was enabled and DCT (Dynamic Connected Transport) was used as the transport layer, while HPC-X version 2.50 was used for MPI. Azure CentOS HPC 7.6 image was used from https://github.com/Azure/azhpc-images
VMs
Cores
PPN
SETime
SpeedUp
ParallelEff
1
116
116
258.92
116
100
2
232
116
129.56
231.82
99.9
4
464
116
62.01
484.35
104.4
16
1856
116
16.46
1824.71
98.3
32
3712
116
8.4
3575.56
96.3
64
7424
116
4.8
6257.23
84.3
128
14848
116
2.5
12013.89
80.9
Summary: Star-CCM+ was scaled at 81% efficiency to nearly 15,000 MPI ranks delivering an application speedup of more than 12,000x. This compares favorably to Azure’s previous best of more than 11,500 MPI ranks, which itself was a world-record for MPI scalability on the public cloud.
App: ANSYS Fluent
Version: 14.06.004
Model: External Flow over a Formula-1 Race Car (f1_racecar_140m)
Configuration Details: 60 MPI ranks were run (2 out of 4 cores per NUMA) in each HBv2 VM in order to leave nominal resources to run Linux background processes and give ~6 GB/s of memory bandwidth per core. In addition, Adaptive Routing was enabled and DCT (Dynamic Connected Transport) was used as the transport layer, while HPC-X version 2.50 was used for MPI. Azure CentOS HPC 7.6 image was used from https://github.com/Azure/azhpc-images
VMs
HBv2 Solver Rating
HBv2 Speedup
Linear Ideal Speedup
1
68.5
1
1
2
134.5
1.96
2
4
275.9
4.03
4
8
557.8
8.14
8
16
1122.1
16.38
16
32
2385.1
34.82
32
64
4601.9
67.18
64
128
9846.2
143.74
128
Summary: HBv2 VMs scale super linearly (112%) up to the top end measured number of VMs (128). The Fluent Solver Rating measured at this top-end level of scale is 83% more performance than the current leader submission on ANSYS public database for this model (https://bit.ly/2OdAExM).
Impact of Adaptive Routing
App: Siemens Star-CCM+
Version: 14.06.004
Model: LeMans 100M Coupled Solver
Configuration Details: Star-CCM+ performance was compared on an “apples to apples” basis, with the only variable as Adaptive Routing being disabled and then enabled.
Summary: Adaptive Routing, designed to drive higher sustained application scalability for large MPI jobs, delivered a scaling efficiency improvement of 17% over an identical job run with the feature disabled. This translates to faster time to solution, and more efficient use of application licenses.
How is the "HBV2 Solver Rating" for Ansys can be interpreted?
Is it referred in seconds or hours?
Regards
Vijay
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Just in time for SC’19, Azure launched into Preview this week the new HBv2 Virtual Machines for High-Performance Computing (HPC).
\n
These VMs feature a wealth of new technology, including:
\n
\n
AMD EPYC 7742 CPUs (Rome)
\n
2.45 GHz Base clock / 3.3 GHz Boost clock
\n
480 MB L3 cache, 480 GB RAM
\n
340 GB/s of Memory Bandwidth
\n
200 Gbps HDR InfiniBand (SRIOV) with Adaptive Routing
\n
900 GB SSD (NVMeDirect)
\n
\n
\n
Below are initial performance characterizations using a variety of configurations on both microbenchmarks as well as commonly used HPC applications for which the HB family of VMs is optimized for.
\n
\n
Microbenchmarks
\n
\n
MPI Latency
\n
OSU Benchmarks (5.6.2) – osu_latency with MPI = HPC-X, Intel MPI, MVAPICH2, OpenMPI
\n
\n
\n\n
\n
\n
Message Size (bytes)
\n
\n
\n
HPC-X
\n
\n
\n
Intel MPI
\n
\n
\n
MVAPICH2
\n
\n
\n
OpenMPI
\n
\n
\n
\n
\n
0
\n
\n
\n
1.62
\n
\n
\n
1.95
\n
\n
\n
1.85
\n
\n
\n
1.61
\n
\n
\n
\n
\n
1
\n
\n
\n
1.62
\n
\n
\n
1.95
\n
\n
\n
1.9
\n
\n
\n
1.61
\n
\n
\n
\n
\n
2
\n
\n
\n
1.61
\n
\n
\n
1.95
\n
\n
\n
1.9
\n
\n
\n
1.61
\n
\n
\n
\n
\n
4
\n
\n
\n
1.62
\n
\n
\n
1.95
\n
\n
\n
1.9
\n
\n
\n
1.61
\n
\n
\n
\n
\n
8
\n
\n
\n
1.61
\n
\n
\n
1.96
\n
\n
\n
1.9
\n
\n
\n
1.61
\n
\n
\n
\n
\n
16
\n
\n
\n
1.62
\n
\n
\n
1.96
\n
\n
\n
1.93
\n
\n
\n
1.62
\n
\n
\n
\n
\n
32
\n
\n
\n
1.77
\n
\n
\n
1.97
\n
\n
\n
1.94
\n
\n
\n
1.77
\n
\n
\n
\n
\n
64
\n
\n
\n
1.83
\n
\n
\n
2.03
\n
\n
\n
2.08
\n
\n
\n
1.82
\n
\n
\n
\n
\n
128
\n
\n
\n
1.9
\n
\n
\n
2.09
\n
\n
\n
2.29
\n
\n
\n
1.9
\n
\n
\n
\n
\n
256
\n
\n
\n
2.44
\n
\n
\n
2.65
\n
\n
\n
2.78
\n
\n
\n
2.44
\n
\n
\n
\n
\n
512
\n
\n
\n
2.53
\n
\n
\n
2.71
\n
\n
\n
2.84
\n
\n
\n
2.53
\n
\n
\n
\n
\n
1024
\n
\n
\n
2.63
\n
\n
\n
2.84
\n
\n
\n
2.93
\n
\n
\n
2.62
\n
\n
\n
\n
\n
2048
\n
\n
\n
2.92
\n
\n
\n
3.09
\n
\n
\n
3.13
\n
\n
\n
2.92
\n
\n
\n
\n
\n
4096
\n
\n
\n
3.72
\n
\n
\n
3.89
\n
\n
\n
4.07
\n
\n
\n
3.74
\n
\n
\n\n
\n
\n
\n
\n
MPI Bandwidth (2 QP’s)
\n
OSU Benchmarks (5.6.2) – osu_mbw_mr with ppn = 2 with MPI = HPC-X
\n
\n
\n\n
\n
\n
#bytes
\n
\n
\n
BW peak
\n
\n
\n
BW average
\n
\n
\n
\n
\n
4096
\n
\n
\n
15920.65
\n
\n
\n
15902.67
\n
\n
\n
\n
\n
8192
\n
\n
\n
23045.57
\n
\n
\n
23036.88
\n
\n
\n
\n
\n
16384
\n
\n
\n
23270.14
\n
\n
\n
23260.04
\n
\n
\n
\n
\n
32768
\n
\n
\n
23376.91
\n
\n
\n
23372.9
\n
\n
\n
\n
\n
65536
\n
\n
\n
23423.49
\n
\n
\n
23423.23
\n
\n
\n
\n
\n
131072
\n
\n
\n
23445.05
\n
\n
\n
23443.6
\n
\n
\n
\n
\n
262144
\n
\n
\n
23463.94
\n
\n
\n
23463.93
\n
\n
\n
\n
\n
524288
\n
\n
\n
23470.7
\n
\n
\n
23470.55
\n
\n
\n
\n
\n
1048576
\n
\n
\n
23474.3
\n
\n
\n
23474.08
\n
\n
\n
\n
\n
2097152
\n
\n
\n
23475.77
\n
\n
\n
23475.73
\n
\n
\n
\n
\n
4194304
\n
\n
\n
23476.61
\n
\n
\n
23476.61
\n
\n
\n
\n
\n
8388608
\n
\n
\n
23477.06
\n
\n
\n
23477.05
\n
\n
\n\n
\n
\n
\n
Application Benchmarks
\n
\n
App: Siemens Star-CCM+
\n
Version: 14.06.004
\n
Model: LeMans 100M Coupled Solver
\n
Configuration Details: 116 MPI ranks were run (4 ranks from each of 29 NUMA) in each HBv2 VM in order to leave nominal resources to run Linux background processes. In addition, Adaptive Routing was enabled and DCT (Dynamic Connected Transport) was used as the transport layer, while HPC-X version 2.50 was used for MPI. Azure CentOS HPC 7.6 image was used from https://github.com/Azure/azhpc-images
\n
\n\n
\n
\n
VMs
\n
\n
\n
Cores
\n
\n
\n
PPN
\n
\n
\n
SETime
\n
\n
\n
SpeedUp
\n
\n
\n
ParallelEff
\n
\n
\n
\n
\n
1
\n
\n
\n
116
\n
\n
\n
116
\n
\n
\n
258.92
\n
\n
\n
116
\n
\n
\n
100
\n
\n
\n
\n
\n
2
\n
\n
\n
232
\n
\n
\n
116
\n
\n
\n
129.56
\n
\n
\n
231.82
\n
\n
\n
99.9
\n
\n
\n
\n
\n
4
\n
\n
\n
464
\n
\n
\n
116
\n
\n
\n
62.01
\n
\n
\n
484.35
\n
\n
\n
104.4
\n
\n
\n
\n
\n
16
\n
\n
\n
1856
\n
\n
\n
116
\n
\n
\n
16.46
\n
\n
\n
1824.71
\n
\n
\n
98.3
\n
\n
\n
\n
\n
32
\n
\n
\n
3712
\n
\n
\n
116
\n
\n
\n
8.4
\n
\n
\n
3575.56
\n
\n
\n
96.3
\n
\n
\n
\n
\n
64
\n
\n
\n
7424
\n
\n
\n
116
\n
\n
\n
4.8
\n
\n
\n
6257.23
\n
\n
\n
84.3
\n
\n
\n
\n
\n
128
\n
\n
\n
14848
\n
\n
\n
116
\n
\n
\n
2.5
\n
\n
\n
12013.89
\n
\n
\n
80.9
\n
\n
\n\n
\n
\n
\n
\n
Summary: Star-CCM+ was scaled at 81% efficiency to nearly 15,000 MPI ranks delivering an application speedup of more than 12,000x. This compares favorably to Azure’s previous best of more than 11,500 MPI ranks, which itself was a world-record for MPI scalability on the public cloud.
\n
\n
\n
\n
\n
App: ANSYS Fluent
\n
Version: 14.06.004
\n
Model: External Flow over a Formula-1 Race Car (f1_racecar_140m)
\n
Configuration Details: 60 MPI ranks were run (2 out of 4 cores per NUMA) in each HBv2 VM in order to leave nominal resources to run Linux background processes and give ~6 GB/s of memory bandwidth per core. In addition, Adaptive Routing was enabled and DCT (Dynamic Connected Transport) was used as the transport layer, while HPC-X version 2.50 was used for MPI. Azure CentOS HPC 7.6 image was used from https://github.com/Azure/azhpc-images
\n
\n
\n\n
\n
\n
VMs
\n
\n
\n
HBv2 Solver Rating
\n
\n
\n
HBv2 Speedup
\n
\n
\n
Linear Ideal Speedup
\n
\n
\n
\n
\n
1
\n
\n
\n
68.5
\n
\n
\n
1
\n
\n
\n
1
\n
\n
\n
\n
\n
2
\n
\n
\n
134.5
\n
\n
\n
1.96
\n
\n
\n
2
\n
\n
\n
\n
\n
4
\n
\n
\n
275.9
\n
\n
\n
4.03
\n
\n
\n
4
\n
\n
\n
\n
\n
8
\n
\n
\n
557.8
\n
\n
\n
8.14
\n
\n
\n
8
\n
\n
\n
\n
\n
16
\n
\n
\n
1122.1
\n
\n
\n
16.38
\n
\n
\n
16
\n
\n
\n
\n
\n
32
\n
\n
\n
2385.1
\n
\n
\n
34.82
\n
\n
\n
32
\n
\n
\n
\n
\n
64
\n
\n
\n
4601.9
\n
\n
\n
67.18
\n
\n
\n
64
\n
\n
\n
\n
\n
128
\n
\n
\n
9846.2
\n
\n
\n
143.74
\n
\n
\n
128
\n
\n
\n\n
\n
\n
\n
Summary: HBv2 VMs scale super linearly (112%) up to the top end measured number of VMs (128). The Fluent Solver Rating measured at this top-end level of scale is 83% more performance than the current leader submission on ANSYS public database for this model (https://bit.ly/2OdAExM).
\n
\n
\n
Impact of Adaptive Routing
\n
\n
App: Siemens Star-CCM+
\n
Version: 14.06.004
\n
Model: LeMans 100M Coupled Solver
\n
Configuration Details: Star-CCM+ performance was compared on an “apples to apples” basis, with the only variable as Adaptive Routing being disabled and then enabled.
\n
\n
\n
\n
Summary: Adaptive Routing, designed to drive higher sustained application scalability for large MPI jobs, delivered a scaling efficiency improvement of 17% over an identical job run with the feature disabled. This translates to faster time to solution, and more efficient use of application licenses.
Just in time for SC’19, Azure launched into Preview this week the new HBv2 Virtual Machines for High-Performance Computing (HPC).
\n
These VMs feature a wealth of new technology, including:
\n
\n
AMD EPYC 7742 CPUs (Rome)
\n
2.45 GHz Base clock / 3.3 GHz Boost clock
\n
480 MB L3 cache, 480 GB RAM
\n
340 GB/s of Memory Bandwidth
\n
200 Gbps HDR InfiniBand (SRIOV) with Adaptive Routing
\n
900 GB SSD (NVMeDirect)
\n
\n
\n
Below are initial performance characterizations using a variety of configurations on both microbenchmarks as well as commonly used HPC applications for which the HB family of VMs is optimized for.
\n
\n
Microbenchmarks
\n
\n
MPI Latency
\n
OSU Benchmarks (5.6.2) – osu_latency with MPI = HPC-X, Intel MPI, MVAPICH2, OpenMPI
\n
\n
\n\n
\n
\n
Message Size (bytes)
\n
\n
\n
HPC-X
\n
\n
\n
Intel MPI
\n
\n
\n
MVAPICH2
\n
\n
\n
OpenMPI
\n
\n
\n
\n
\n
0
\n
\n
\n
1.62
\n
\n
\n
1.95
\n
\n
\n
1.85
\n
\n
\n
1.61
\n
\n
\n
\n
\n
1
\n
\n
\n
1.62
\n
\n
\n
1.95
\n
\n
\n
1.9
\n
\n
\n
1.61
\n
\n
\n
\n
\n
2
\n
\n
\n
1.61
\n
\n
\n
1.95
\n
\n
\n
1.9
\n
\n
\n
1.61
\n
\n
\n
\n
\n
4
\n
\n
\n
1.62
\n
\n
\n
1.95
\n
\n
\n
1.9
\n
\n
\n
1.61
\n
\n
\n
\n
\n
8
\n
\n
\n
1.61
\n
\n
\n
1.96
\n
\n
\n
1.9
\n
\n
\n
1.61
\n
\n
\n
\n
\n
16
\n
\n
\n
1.62
\n
\n
\n
1.96
\n
\n
\n
1.93
\n
\n
\n
1.62
\n
\n
\n
\n
\n
32
\n
\n
\n
1.77
\n
\n
\n
1.97
\n
\n
\n
1.94
\n
\n
\n
1.77
\n
\n
\n
\n
\n
64
\n
\n
\n
1.83
\n
\n
\n
2.03
\n
\n
\n
2.08
\n
\n
\n
1.82
\n
\n
\n
\n
\n
128
\n
\n
\n
1.9
\n
\n
\n
2.09
\n
\n
\n
2.29
\n
\n
\n
1.9
\n
\n
\n
\n
\n
256
\n
\n
\n
2.44
\n
\n
\n
2.65
\n
\n
\n
2.78
\n
\n
\n
2.44
\n
\n
\n
\n
\n
512
\n
\n
\n
2.53
\n
\n
\n
2.71
\n
\n
\n
2.84
\n
\n
\n
2.53
\n
\n
\n
\n
\n
1024
\n
\n
\n
2.63
\n
\n
\n
2.84
\n
\n
\n
2.93
\n
\n
\n
2.62
\n
\n
\n
\n
\n
2048
\n
\n
\n
2.92
\n
\n
\n
3.09
\n
\n
\n
3.13
\n
\n
\n
2.92
\n
\n
\n
\n
\n
4096
\n
\n
\n
3.72
\n
\n
\n
3.89
\n
\n
\n
4.07
\n
\n
\n
3.74
\n
\n
\n\n
\n
\n
\n
\n
MPI Bandwidth (2 QP’s)
\n
OSU Benchmarks (5.6.2) – osu_mbw_mr with ppn = 2 with MPI = HPC-X
\n
\n
\n\n
\n
\n
#bytes
\n
\n
\n
BW peak
\n
\n
\n
BW average
\n
\n
\n
\n
\n
4096
\n
\n
\n
15920.65
\n
\n
\n
15902.67
\n
\n
\n
\n
\n
8192
\n
\n
\n
23045.57
\n
\n
\n
23036.88
\n
\n
\n
\n
\n
16384
\n
\n
\n
23270.14
\n
\n
\n
23260.04
\n
\n
\n
\n
\n
32768
\n
\n
\n
23376.91
\n
\n
\n
23372.9
\n
\n
\n
\n
\n
65536
\n
\n
\n
23423.49
\n
\n
\n
23423.23
\n
\n
\n
\n
\n
131072
\n
\n
\n
23445.05
\n
\n
\n
23443.6
\n
\n
\n
\n
\n
262144
\n
\n
\n
23463.94
\n
\n
\n
23463.93
\n
\n
\n
\n
\n
524288
\n
\n
\n
23470.7
\n
\n
\n
23470.55
\n
\n
\n
\n
\n
1048576
\n
\n
\n
23474.3
\n
\n
\n
23474.08
\n
\n
\n
\n
\n
2097152
\n
\n
\n
23475.77
\n
\n
\n
23475.73
\n
\n
\n
\n
\n
4194304
\n
\n
\n
23476.61
\n
\n
\n
23476.61
\n
\n
\n
\n
\n
8388608
\n
\n
\n
23477.06
\n
\n
\n
23477.05
\n
\n
\n\n
\n
\n
\n
Application Benchmarks
\n
\n
App: Siemens Star-CCM+
\n
Version: 14.06.004
\n
Model: LeMans 100M Coupled Solver
\n
Configuration Details: 116 MPI ranks were run (4 ranks from each of 29 NUMA) in each HBv2 VM in order to leave nominal resources to run Linux background processes. In addition, Adaptive Routing was enabled and DCT (Dynamic Connected Transport) was used as the transport layer, while HPC-X version 2.50 was used for MPI. Azure CentOS HPC 7.6 image was used from https://github.com/Azure/azhpc-images
\n
\n\n
\n
\n
VMs
\n
\n
\n
Cores
\n
\n
\n
PPN
\n
\n
\n
SETime
\n
\n
\n
SpeedUp
\n
\n
\n
ParallelEff
\n
\n
\n
\n
\n
1
\n
\n
\n
116
\n
\n
\n
116
\n
\n
\n
258.92
\n
\n
\n
116
\n
\n
\n
100
\n
\n
\n
\n
\n
2
\n
\n
\n
232
\n
\n
\n
116
\n
\n
\n
129.56
\n
\n
\n
231.82
\n
\n
\n
99.9
\n
\n
\n
\n
\n
4
\n
\n
\n
464
\n
\n
\n
116
\n
\n
\n
62.01
\n
\n
\n
484.35
\n
\n
\n
104.4
\n
\n
\n
\n
\n
16
\n
\n
\n
1856
\n
\n
\n
116
\n
\n
\n
16.46
\n
\n
\n
1824.71
\n
\n
\n
98.3
\n
\n
\n
\n
\n
32
\n
\n
\n
3712
\n
\n
\n
116
\n
\n
\n
8.4
\n
\n
\n
3575.56
\n
\n
\n
96.3
\n
\n
\n
\n
\n
64
\n
\n
\n
7424
\n
\n
\n
116
\n
\n
\n
4.8
\n
\n
\n
6257.23
\n
\n
\n
84.3
\n
\n
\n
\n
\n
128
\n
\n
\n
14848
\n
\n
\n
116
\n
\n
\n
2.5
\n
\n
\n
12013.89
\n
\n
\n
80.9
\n
\n
\n\n
\n
\n
\n
\n
Summary: Star-CCM+ was scaled at 81% efficiency to nearly 15,000 MPI ranks delivering an application speedup of more than 12,000x. This compares favorably to Azure’s previous best of more than 11,500 MPI ranks, which itself was a world-record for MPI scalability on the public cloud.
\n
\n
\n
\n
\n
App: ANSYS Fluent
\n
Version: 14.06.004
\n
Model: External Flow over a Formula-1 Race Car (f1_racecar_140m)
\n
Configuration Details: 60 MPI ranks were run (2 out of 4 cores per NUMA) in each HBv2 VM in order to leave nominal resources to run Linux background processes and give ~6 GB/s of memory bandwidth per core. In addition, Adaptive Routing was enabled and DCT (Dynamic Connected Transport) was used as the transport layer, while HPC-X version 2.50 was used for MPI. Azure CentOS HPC 7.6 image was used from https://github.com/Azure/azhpc-images
\n
\n
\n\n
\n
\n
VMs
\n
\n
\n
HBv2 Solver Rating
\n
\n
\n
HBv2 Speedup
\n
\n
\n
Linear Ideal Speedup
\n
\n
\n
\n
\n
1
\n
\n
\n
68.5
\n
\n
\n
1
\n
\n
\n
1
\n
\n
\n
\n
\n
2
\n
\n
\n
134.5
\n
\n
\n
1.96
\n
\n
\n
2
\n
\n
\n
\n
\n
4
\n
\n
\n
275.9
\n
\n
\n
4.03
\n
\n
\n
4
\n
\n
\n
\n
\n
8
\n
\n
\n
557.8
\n
\n
\n
8.14
\n
\n
\n
8
\n
\n
\n
\n
\n
16
\n
\n
\n
1122.1
\n
\n
\n
16.38
\n
\n
\n
16
\n
\n
\n
\n
\n
32
\n
\n
\n
2385.1
\n
\n
\n
34.82
\n
\n
\n
32
\n
\n
\n
\n
\n
64
\n
\n
\n
4601.9
\n
\n
\n
67.18
\n
\n
\n
64
\n
\n
\n
\n
\n
128
\n
\n
\n
9846.2
\n
\n
\n
143.74
\n
\n
\n
128
\n
\n
\n\n
\n
\n
\n
Summary: HBv2 VMs scale super linearly (112%) up to the top end measured number of VMs (128). The Fluent Solver Rating measured at this top-end level of scale is 83% more performance than the current leader submission on ANSYS public database for this model (https://bit.ly/2OdAExM).
\n
\n
\n
Impact of Adaptive Routing
\n
\n
App: Siemens Star-CCM+
\n
Version: 14.06.004
\n
Model: LeMans 100M Coupled Solver
\n
Configuration Details: Star-CCM+ performance was compared on an “apples to apples” basis, with the only variable as Adaptive Routing being disabled and then enabled.
\n
\n
\n
\n
Summary: Adaptive Routing, designed to drive higher sustained application scalability for large MPI jobs, delivered a scaling efficiency improvement of 17% over an identical job run with the feature disabled. This translates to faster time to solution, and more efficient use of application licenses.
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How is the \"HBV2 Solver Rating\" for Ansys can be interpreted?
Is it referred in seconds or hours?
Regards
Vijay
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