RADIOSS 13.0Performance Benchmark and Profiling

April 2015

2

Note

• The following research was performed under the HPC Advisory Council activities

– Participating vendors: Intel, Dell, Mellanox

– Compute resource - HPC Advisory Council Cluster Center

• The following was done to provide best practices

– RADIOSS performance overview

– Understanding RADIOSS communication patterns

– Ways to increase RADIOSS productivity

– MPI libraries comparisons

• For more info please refer to

– http://www.altair.com

– http://www.dell.com

– http://www.intel.com

– http://www.mellanox.com

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Objectives

• The following was done to provide best practices– RADIOSS performance benchmarking

– Interconnect performance comparisons

– MPI performance comparison

– Understanding RADIOSS communication patterns

• The presented results will demonstrate – The scalability of the compute environment to provide nearly linear

application scalability

– The capability of RADIOSS to achieve scalable productivity

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RADIOSS by Altair

• Altair RADIOSS

– Structural analysis solver for highly non-linear problems under dynamic loadings

– Consists of features for:

• multiphysics simulation and advanced materials such as composites

– Highly differentiated for Scalability, Quality and Robustness

• RADIOSS is used across all industry worldwide

– Improves crashworthiness, safety, and manufacturability of structural designs

• RADIOSS has established itself as an industry standard

– for automotive crash and impact analysis for over 20 years

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Test Cluster Configuration

• Dell™ PowerEdge™ R730 32-node (896-core) “Thor” cluster

– Dual-Socket 14-core Intel E5-2697v3 @ 2.60 GHz CPUs (Turbo on, Max Perf set in BIOS)

– OS: RHEL 6.5, OFED MLNX_OFED_LINUX-2.4-1.0.5 InfiniBand SW stack

– Memory: 64GB memory, DDR3 2133 MHz

– Hard Drives: 1TB 7.2 RPM SATA 2.5”

• Mellanox Switch-IB SB7700 100Gb/s InfiniBand VPI switch

• Mellanox ConnectX-4 EDR 100Gb/s InfiniBand VPI adapters

• Mellanox ConnectX-3 40/56Gb/s QDR/FDR InfiniBand VPI adapters

• Mellanox SwitchX SX6036 56Gb/s FDR InfiniBand VPI switch

• MPI: Intel MPI 5.0.2, Mellanox HPC-X v1.2.0

• Application: Altair RADIOSS 13.0

• Benchmark datasets:

– Neon benchmarks: 1 million elements (8ms, Double Precision), unless otherwise stated

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About Intel® Cluster Ready

• Intel® Cluster Ready systems make it practical to use a cluster to increase

your simulation and modeling productivity

– Simplifies selection, deployment, and operation of a cluster

• A single architecture platform supported by many OEMs, ISVs, cluster

provisioning vendors, and interconnect providers

– Focus on your work productivity, spend less management time on the cluster

• Select Intel Cluster Ready

– Where the cluster is delivered ready to run

– Hardware and software are integrated and configured together

– Applications are registered, validating execution on the Intel Cluster Ready

architecture

– Includes Intel® Cluster Checker tool, to verify functionality and periodically check

cluster health

• RADIOSS is Intel Cluster Ready

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PowerEdge R730Massive flexibility for data intensive operations

• Performance and efficiency

– Intelligent hardware-driven systems management

with extensive power management features

– Innovative tools including automation for

parts replacement and lifecycle manageability

– Broad choice of networking technologies from GbE to IB

– Built in redundancy with hot plug and swappable PSU, HDDs and fans

• Benefits

– Designed for performance workloads

• from big data analytics, distributed storage or distributed computing

where local storage is key to classic HPC and large scale hosting environments

• High performance scale-out compute and low cost dense storage in one package

• Hardware Capabilities

– Flexible compute platform with dense storage capacity

• 2S/2U server, 6 PCIe slots

– Large memory footprint (Up to 768GB / 24 DIMMs)

– High I/O performance and optional storage configurations

• HDD options: 12 x 3.5” - or - 24 x 2.5 + 2x 2.5 HDDs in rear of server

• Up to 26 HDDs with 2 hot plug drives in rear of server for boot or scratch

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RADIOSS Performance – Interconnect (MPP)

• EDR InfiniBand provides better scalability performance than Ethernet

– 70 times better performance than 1GbE at 16 nodes / 448 cores

– 4.8x better performance than 10GbE at 16 nodes / cores

– Ethernet solutions does not scale beyond 4 nodes with pure MPI

28 Processes/Node

4.8x

Intel MPI

Higher is better

70x

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RADIOSS Performance – Interconnect (MPP)

• EDR InfiniBand provides better scalability performance

– EDR InfiniBand improves over QDR IB by 28% at 16 nodes / 448 cores

– Similarly, EDR InfiniBand outperforms FDR InfiniBand by 25% at 16 nodes

28 Processes/Node

25%28%

Higher is better

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RADIOSS Performance – CPU Cores

• Running more cores per node generally improves overall performance

– Seen improvement of 18% from 20 to 28 cores per node at 8 nodes

– Improvement seems not as consistent at higher node counts

• Guideline: Most optimal workload distribution is 4000 elements/process

– For test case of 1 million elements, most optimal core sizes is ~256 cores

– 4000 elements per process should provides sufficient workload for each process

– Hybrid MPP (HMPP) provides way to achieve additional scalability on more CPUs

6%

Intel MPIHigher is better

18%

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RADIOSS Performance – Simulation Time

• Increasing simulation time increase the run time at a faster rate

– Increasing a 8ms simulation to 80ms can result in much longer runtime

– 10x longer simulation run can result in a 14x in the runtime

– Contacts usually become more severe at the middle of the run,

so it costs more complexity and CPU utilization and therefore cost/cycle increases

Intel MPIHigher is better

14x

14x

13x

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RADIOSS Profiling – % Time Spent on MPI

• RADIOSS utilizes point-to-point communications in most data transfers

• The most time MPI consuming calls is MPI_Waitany() and MPI_Wait()

– MPI_Recv(55%), MPI_Waitany(23%), MPI_Allreduce(13%)

28 Processes/NodeMPP Mode

13

RADIOSS Performance – IMPI Tuning (MPP)

• Tuning Intel MPI collective algorithm can improve performance

– MPI profile shows about 20% of runtime spent on MPI_Allreduce communications

– Default algorithm in Intel MPI is Recursive Doubling

– The default algorithm is the best among all tested for MPP

Intel MPI

Higher is better 28 Processes/Node

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RADIOSS Performance – MPI Libraries (MPP)

• Both HPC-X and Intel MPI performs similarly

– MPI profile shows about 20% of MPI time spent on Allreduce communications

– MPI collective operations (such as Allreduce) can potentially be optimized

– Support for Open MPI is new for RADIOSS

– HPC-X is a tuned MPI distribution based on the latest Open MPI

Higher is better 28 Processes/Node

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RADIOSS Hybrid MPP Parallelization

• Highly parallel code

– Multi-level parallelization

– Domain decomposition MPI parallelization

– Multithreading OpenMP

• Enhanced performance

– Best scalability in the marketplace

– High efficiency on large HPC clusters

– Unique, proven method for rich scalability over thousands of cores for FEA

– Flexibility -- easy tuning of MPI & OpenMP

– Robustness -- parallel arithmetic allows perfect repeatability in parallel

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RADIOSS Performance – Hybrid MPP version

• Enabling Hybrid MPP mode unlocks the RADIOSS scalability

– At larger scale, productivity improves as more threads involves

– As more threads involved, amount of communications by processes are reduced

– At 32 nodes/896 cores, best configuration is 1 process per socket to spawn 14 threads each

– 28 threads/1 PPN is not advised due to breach of data locality across different CPU socket

• The following environment setting and tuned flags are used for Intel MPI:

– I_MPI_PIN_DOMAIN auto

– I_MPI_ADJUST_ALLREDUCE 5

– I_MPI_ADJUST_BCAST 1

– KMP_AFFINITY compact

– KMP_STACKSIZE 400m

– ulimit -s unlimited

EDR InfiniBand

Intel MPI

3.7x

32%70%

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RADIOSS Profiling – Number of MPI Calls

• For MPP utilizes most non-blocking calls for communications

– MPI_Recv, MPI_Waitany, MPI_Allreduce are used most of the time

• For HMPP, communication behavior has changed

– Higher time percentage in MPI_Waitany, MPI_Allreduce, and MPI_Recv

• MPI Communication behavior changed from previous RADIOSS version

– Most likely due to more CPU cores available on the current cluster

At 32 Nodes

HMPP, 2PPN / 14 ThreadsMPP, 28PPN

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RADIOSS Profiling – MPI Message Sizes

• The most time consuming MPI communications are:

– MPI_Recv: Messages concentrated at 640B, 1KB, 320B, 1280B

– MPI_Waitany: Messages are: 48B, 8B, 384B

– MPI_Allreduce: Most message sizes appears at 80B

28 Processes/NodePure MPP

HMPP, 2PPN / 14 ThreadsMPP, 28PPN

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RADIOSS Performance – Intel MPI Tuning (DP)

• For Hybrid MPP DP, tuning MPI_Allreduce shows more gain than MPP

– For DAPL provider, Binomial gather+scatter #5 improved perf by 27% over default

– For OFA provider, tuned MPI_Allreduce algorithm improves by 44% over default

– Both OFA and DAPL improved by tuning I_MPI_ADJUST_ALLREDUCE=5

– Flags for OFA: I_MPI_OFA_USE_XRC 1. For DAPL: ofa-v2-mlx5_0-1u provider

27%

Intel MPI

Higher is better 2 PPN / 14 OpenMP

44%

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RADIOSS Performance – Interconnect (HMPP)

• EDR InfiniBand provides better scalability performance than Ethernet

– 214% better performance than 1GbE at 16 nodes

– 104% better performance than 10GbE at 16 nodes

– InfiniBand typically outperforms other interconnect in collective operations

214%

104%

2 PPN / 14 OpenMP

Intel MPI

Higher is better

21

RADIOSS Performance – Interconnect (HMPP)

• EDR InfiniBand provides better scalability performance than FDR IB

– EDR IB outperforms FDR IB by 27% at 32 nodes

– Improvement for EDR InfiniBand occurs at high node count

27%

2 PPN / 14 OpenMP

Intel MPI

Higher is better

22

RADIOSS Performance–Floating Point Precision

• Single precision job runs faster double precision

– SP provides 47% speedup than DP

– Similar scalability is seen for double precision tests

47%

Intel MPI

Higher is better 2 PPN / 14 OpenMP

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RADIOSS Performance – CPU Frequency

• Increasing CPU core frequency enables higher job efficiency

– 18% of performance jump from 2.3GHz to 2.6GHz (13% increase in clock speed)

– 29% of performance jump from 2.0GHz to 2.6GHz (30% increase in clock speed)

• Increase in performance gain exceeds the increase CPU frequencies

– CPU bound application see higher benefit of using CPU with higher frequencies

29%

Intel MPI

Higher is better 2 PPN / 14 OpenMP

18%

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RADIOSS Performance – System Generations

• Intel E5-2680v3 (Haswell) cluster outperforms prior generations

– Performs faster by 100% vs Jupiter, by 238% vs Janus at 16 nodes

• System components used:

– Thor: 2-socket Intel E5-2680v3@2.6GHz, 2133MHz DIMMs, EDR IB, v13.0

– Jupiter: 2-socket Intel E5-2680@2.7GHz, 1600MHz DIMMs, FDR IB, v12.0

– Janus: 2-socket Intel X5670@2.93GHz, 1333MHz DIMMs, QDR IB, v12.0

Single Precision

100%

238%

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RADIOSS Profiling – Memory Required

• Differences in memory consumption between MPP and HMPP

– MPP: Memory required to run the workload is ~5GB per node

– HMPP: Approximately 400MB per node is needed (as there are 2 PPN)

– Considered as a small workload but good enough to observe application behavior

28 CPU Cores/NodeAt 32 Nodes

HMPP, 2PPN / 14 ThreadsMPP, 28PPN

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RADIOSS – Summary

• RADIOSS is designed to perform at large scale HPC environment

– Shows excellent scalability over 896 cores/32 nodes and beyond with Hybrid MPP

– Hybrid MPP version enhanced RADIOSS scalability

• 2 MPI processes per node (or 1 MPI process per socket), 14 threads each

– Additional CPU cores generally accelerating time to solution performance

– Intel E5-2680v3 (Haswell) cluster outperforms prior generations

• Performs faster by 100% vs “Sandy Bridge”, by 238% vs “Westmere “ at 16 nodes

• Network and MPI Tuning

– EDR InfiniBand outperforms other Ethernet-based interconnects in scalability

– EDR InfiniBand delivers higher scalability performance than FDR and QDR IB

– Tuning environment parameters is important to maximize performance

– Tuning MPI collective ops helps RADIOSS to achieve even better scalability

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Thank YouHPC Advisory Council

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