Performance Characteristics of Large SMP Machines
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1 Performance Characteristics of Large SMP Machines Dirk Schmidl, Dieter an Mey, Matthias S. Müller Rechen- und Kommunikationszentrum (RZ)
2 Agenda Investigated Hardware Kernel Benchmark Results Memory Bandwidth NUMA Distances Synchronizations Applications NestedCP TrajSearch Conclusion 2
3 Hardware HP ProLiant DL98 G7 8 x Intel Xeon 2 GHz 256 GB main memory internally several boards SGI Altix Ultraviolet 14 x Intel Xeon 2.4 GHz about 2 TB main memory 2 Socket Boards connected with NUMALink network Bull Coherence Switch System 16 x Intel Xeon 2 GHz 256 GB main memory 3 4-socket boards externally connected with the Bull Coherence Switch (BCS)
4 Hardware ScaleMP System 64 x Intel Xeon 2 GHz about 4 TB main memory 4-socket boards connected with Infiniband vsmp foundation software used to create a cache coherent single system Intel Xeon Phi 1 Intel Xeon Phi 1.5 GHz plugged in a PCIe slot 8 GB main memory 4
5 1B 16B 256B 4KB 64KB 1MB 16MB 256MB 4GB 1B 16B 256B 4KB 64KB 1MB 16MB 256MB 4GB Bandwidth in GB/s Bandwidth in GB/s 1B 16B 256B 4KB 64KB 1MB 16MB 256MB 4GB 1B 16B 256B 4KB 64KB 1MB 16MB 256MB 4GB 1B 16B 256B 4KB 64KB 1MB 16MB 256MB 4GB Bandwidth in GB/s Bandwidth in GB/s Bandwidth in GB/s Serial Bandwidth Write Bandwidth HP Write Bandwidth BCS Write Bandwidth AltixUV Write Bandwidth ScaleMP Write Bandwidth Phi local remote 1st level remote 2nd level 5 Standard SW-Prefetching
6 Distance Matrix Measured bandwidth between sockets memory and threads placed with numactl normalized to 1 for socket BCS Socket remote accesses much more expensive on the BCS machine HP machine internally has also several NUMA levels HP Socket
7 Bandwidth in GB/s Parallel Bandwidth Number of Threads HP-read ALTIX-read BCS-read SCALEMP-read Phi-read HP-write ALTIX-write BCS-write SCALEMP-write Phi-write Read and Write Bandwidth on local data 16 MB memory footprint per thread 7
8 mem_go_around Investigate slow-down, when remote accesses ocure Every thread initializes local memory and measures the bandwidth In step n thread t uses the memory of thread (t+n)%nthreads this increases the number of remote accesses in every step 8
9 Memory Bandwidth in GB/s mem_go_around Turn HP Altix BCS ScaleMP Phi 9
10 Synchronization Overhead in microseconds to acquire a lock #threads BCS SCALEMP PHI ALTIX HP / / / Synchronization overhead rises with the number of threads ScaleMP introduces more overhead for large thread counts 1
11 NestedCP: Parallel Critical Point Extraction Virtual Reality Group of RWTH Aachen University: Analysis of large-scale flow simulations Feature extraction from raw data Interactive analysis in virtual environment (e.g. a cave) Critical Point: Point in the vector field with zero velocity Andreas Gerndt, Virtual Reality Center, RWTH Aachen 11
12 /6 128/12 24 Runtime in sec. Speedup NestedCP parallelization done with OpenMP tasks many independent tasks only synchronized at the end Number of Threads BCS SCALEMP PHI ALTIX HP BCS-Speedup SCALEMP-Speedup PHI-Speedup ALTIX-Speedup HP-Speedup 12
13 TrajSearch Direct Numerical Simulation of three dimensional turbulent flow field produces large output arrays ~ ½ year of computation produced 248³ output grid (32GB) Trajectory Analysis (TrajSearch) implemented with OpenMP was optimized for large NUMA machines Here the 124³ grid cells data was used (~4 GB) Institute for Combustion Technology 13
14 Runtime in hours Speedup TrajSearch Optimizations: reduced number of locks NUMA aware data initialization data blocked to 8x8x8 blocks to load nearest data on ScaleMP self-written NUMA aware scheduler Number of Threads ALTIX BCS SCALEMP ALTIX-Speedup BCS-Speedup SCALEMP-Speedup 14
15 Conclusion larger systems provide a larger total memory bandwidth the overhead for a lot of remote accesses is also higher on larger systems as has been seen with the mem_go_around test the caching in the vsmp software can hide the remote latency, even when larger arrays are read or written remotely synchronization is a problem on all systems that increases with the number of cores the Xeon Phi system delivers a good bandwidth and low synchronization overhead for a large number of threads applications can run well on large NUMA machines Remark: A Revised version with newer performance measurements will soon be available on our website under publications: 15
16 Conclusion larger systems provide a larger total memory bandwidth the overhead for a lot of remote accesses is also higher on larger systems as has been seen with the mem_go_around test the caching in the vsmp software can hide the remote latency, even when larger arrays are read or written remotely Thank you for your attention! synchronization is a problem on all systems that increases with the Questions? number of cores the Xeon Phi system delivers a good bandwidth and low synchronization overhead for a large number of threads applications can run well on large NUMA machines Remark: A Revised version with newer performance measurements will soon be available on our website under publications: 16
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