Performance analysis with Periscope
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1 Performance analysis with Periscope M. Gerndt, V. Petkov, Y. Oleynik, S. Benedict Technische Universität March 2010
2 Outline Motivation Periscope (PSC) Periscope performance analysis model Tool architecture Performance analysis automation Periscope GUI
3 Motivation Current performance analysis procedure on POWER6: Use Tprof to pinpoint time consuming subroutines Use Xprofiler (GUI for gprof) to understand call graph Use hpmcount (libhpm) to measure Hardware Counters Use mpitrace to investigate mpi communication Problems: Routine, time consuming Error prone Not scalable Requires deep hardware knowledge Solution: Performance analysis automation
4 Periscope Iterative online analysis Measurements are configured, obtained and evaluated on the fly no tracing! Distributed architecture Analysis performed by multiple distributed hierarchical agents Automatic bottlenecks search Based on performance optimization experts' knowledge Enhanced GUI Eclipse based integrated development and performance analysis environment Source-to-source Instrumentation Fortran, C/C++ limitations: multiple source folders, very picky about following standards
5 Iterative online analysis model GUI Analysis Agents Instrumented Application Start Candidate Properties Monitoring Requests Final Properties Report Refinement Location Precision Performance Measurements Proven Properties Raw performance data Analysis
6 Distributed architecture Graphical User Interface Interactive frontend Eclipse-based GUI Analysis control Agents network Monitoring Request Interface Application
7 Automatic search for bottlenecks Automation based on formalized expert knowledge: Potential performance problems properties Efficient search algorithm search strategies Performance property Condition Confidence Severity Performance analysis strategies Itanium2 Stall Cycle Analysis IBM POWER6 Single Core Performance Analysis MPI Communication Pattern Analysis OpenMP
8 POWER6 Single Core Performance Properties Hot spot of the application Memory access pattern Cycles lost due to cache misses Average amount of cycles lost per L1 miss High L1 demand load miss rate High L2 demand load miss rate High L3 demand load miss rate Cycles lost due to address translation misses Cycles lost due to store instructions Cycles lost due to Floating Point instructions inefficiencies Cycles lost due to Integer multiplications and divisions Cycles lost due to no instruction to dispatch
9 Itanium2 Stall Cycle properties IA64 Pipeline Stall Cycles Stalls due to pipeline flush Stalls due to branch misprediction flush Stalls due to exception flush Stalls due to floating point exceptions or L1D TLB misses Stalls due to Flush to zero or SIR stalls Stalls due to L1D TLB misses... Stalls due to waiting for data delivery to register Stalls due to waiting for integer register Stalls due to waiting for integer results Stalls due to waiting for FP register Stalls due to waiting for integer loads L3 misses dominate data access L2 misses L3 misses Stalls due to register stack engine...
10 MPI Communication Patterns Analysis Automatic detection of wait patterns Measurement on the fly No tracing required! p1 MPI_Recv p2 MPI_Send
11 MPI Performance Properties Excessive MPI time in receive due to late sender Excessive MPI time due to late root in broadcast Excessive MPI time in root due to late process in reduce Excessive MPI time in... (1xN, Nx1, 1x1, NxN) Excessive MPI time due to many small messages Excessive MPI communication time
12 Graphical User Interface Source code view Project view SIR outline view Properties view
13 Properties summarization Metaproperties Clustering support Property 1 Cluster 2 Needed for peta-scale PA Cluster 1 Identify hidden behavior Cluster 3 Property 2 Property 3 Cluster 1 CPUs: 7-10,16 Cluster 2 CPUs: 2-3,5,11,13-14 Cluster 3 CPUs:1,4,6,12,15
14 Thank you for your attention! Current version 1.2 Available under: Supported architectures SGI Altix 4700 Itanium2 IBM Power575 POWER6 x86-based architectures Further information: Periscope web page:
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