11ġ2 SGI Perfboost Results on ICE X Clusters The SGI Custom abaqus_v6.env site file is transparent to the user The user can still override or add settings to the Abaqus environment using a local abaqus_v6.env file Works with Abaqus 6.14 and below releases to Abaqus 6.8 Purple line is SGI tuning over IBM PMPI using -aff=automatic:bandwidth - affcycle=numa Blue line is SGI tuning over the Abaqus Defaults Runtime in HH:MM:SS (lower is better) 2:52:48 2:24:00 1:55:12 1:26:24 0:57:36 0:28:48 0:00:00 Automotive Model on ICE X Cluster E v3 2.60GHz 128GB Memory, Abaqus M Elements, 6.8M DOF, No. Using SGI PerfBoost on SGI UV Systems can be 2x or more faster and about 15-32% faster on ICE X Clusters. The mathematical solution is the same but with faster turnaround times.
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The PerfBoost shim library only intercepts and optimizes MPI calls while other application routines execute without intervention. 6ġ0 Performance Tuning SGI Performance Suite MPI/OpenMP Communication Layers: Optimization of Hybrid MPI and OpenMP threads Abaqus Process Thread Placement: Autodetection of UV SMP and distributed clusters to bind application threads to mitigate migration across the system Improve batch Job efficiency: Create batch scheduler topology resources so jobs run more efficient on various SGI platforms Improve Abaqus/Standard execution environment: Leveraging SGI tools to reduce turnaround times by using SGI Performance Suite and SGI s Perfboost MPI layer intercept for both SGI UV SMP distributed clusters Abaqus/Explicit execution environment: Was enhanced to use the SGI Performance Suite with SGI s Perfboost MPI layer 10ġ1 Why SGI Perfboost for Abaqus SGI PerfBoost and SGI MPI provides an enhanced MPI communication layer and tools for tight control of process placement in hybrid MPI/OpenMP environments The bundled IBM Platform MPI (default MPI) implementation for Abaqus FEA lacks the necessary tools for thread affinity when scaling to large core counts on clusters and SGI UV Systems.
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**Comparison to IBM, Cray, Bull and HP based on the number of megaflops per watt in non-accelerated x86-based systems. IBM, Bull, HP and Cray systems.** IP-113 (Dakota) blade for D-Rack 172 teraflops per rack of 1728 processor cores Intel Xeon E v3 Twelve Cores 2.6GHz Houses up to two 2.5 SATA drives for local swap/ scratch usage Memory per core guidance Implicit CSM codes- 4-8GB per core Explicit CSM codes- 2-4GB per core CFD codes 2-4GB per core Integrated Infiniband FDR interconnect Hypercube/Fat Tree Single or Dual-plane network topology Multi-rail network (MPI communications isolated from the NFS traffic, splitting of large messages across 2 rails) Integrated shared storage available SUSE or RHEL with SGI Performance Suite SGI ICE X *Efficiency calculation based upon the number of megaflops per watt in non-accelerated x86-based systems.
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6 Reference Configuration SGI ICE X SGI ICE X Achieves Top 4 out of 5 Most Efficient Supercomputers on TOP500 List *(June 24, 2014) SGI ICE X systems can achieve 20 to 40+% higher performance efficiency vs.