statistical performance analysis for scientific applications
DESCRIPTION
Statistical Performance Analysis for Scientific Applications. Fei Xing • Haihang You • Charng-Da Lu. July 15, 2014. Presentation at the XSEDE14 Conference Atlanta, GA. Running Time Analysis. Causes of slow run on supercomputer Improper memory usage Poor parallelism Too much I/O - PowerPoint PPT PresentationTRANSCRIPT
Statistical Performance Analysis for Scientific Applications
Presentation at the XSEDE14 ConferenceAtlanta, GA
Fei Xing • Haihang You • Charng-Da Lu
July 15, 2014
2
Running Time Analysis
• Causes of slow run on supercomputer– Improper memory usage– Poor parallelism– Too much I/O– Not optimize the program efficiently– …
• Examine user’s code: profiling tools
• Profiling = physical exam for applications– Communication – Fast Profiling library for MPI (FPMPI)– Processor & memory – Performance Application Programming
Interface (PAPI)– Overall performance & Optimization opportunity – CrayPat
3
Profiling Reports
• Profiling tools produce comprehensive reports covering a wider spectrum of application performance
• Imagine, as a scientist and supercomputer user, you see…
• Question: how to make sense of these information from the report?– Meaning of the variables – Indication of the numbers
I/O read timeI/O write time
MPI communication timeMPI synchronization time MPI calls
Level 1 Cache miss
Memory usage
TLB miss L1 Cache access
MPI imbalance
MPI communication imbalance
More are coming!!!
4
Research Framework
• Select an HPC benchmark to create baseline kernels
• Use profiling tools to capture the peak performance
• Apply statistical approach to extract synthetic features that are easy to interpret
• Run real applications, and compare their performance with “role models”
How about…
Courtesy of C.-D. Lu
5
Gears for the Experiment
• Benchmarks – HPC Challenge (HPCC) – Gauge supercomputers toward peak performance– 7 representative kernels:
• DGEMM, FFT, HPL, Random Access, PTRANS, Latency Bandwidth, Stream• HPL is used in the TOP 500 ranking
– 3 parallelism regimes• Serial / Single Processor• Embarrassingly Parallel• MPI Parallel
• Profiling tools – FPMPI and PAPI
• Testing environment – Kraken (Cray XT5)
6
HPCC
Mode 1 means serial/single processor, * means embarrassingly parallel, M means MPI parallel
7
Training Set Design
• 2,954 observations– Various kernels, wide range of matrix sizes, different compute nodes
• 11 performance metrics – gathered from FPMPI and PAPI– MPI communication time, MPI synchronization time, MPI calls, total
MPI bytes, memory, FLOPS, total instructions, L2 data cache access, L1 data cache access, synchronization imbalance, communication imbalance
• Data preprocessing– Convert some metrics to unit-less rates: divide by wall-time– Normalization
FLOPS Memory … MPI calls
HPL_1000
*_FFT_2000
…
M_RA_300,000
Performance Metrics
Obs.
8
Extract Synthetic Features
• Extract synthetic & accessible Performance Indices (PIs)
• Solution: Variable Clustering + Principal Component Analysis (PCA)
• PCA: decorrelate the data
• Problem of using PCA alone: variables with small loadings may over influence the PC score
• Standardization & modified PCA do not work well
9
Variable Clustering
• Given a partition of X, Pk = (C1, …, Ck)
• Centroid of cluster Ci
– is the Pearson Correlation– is 1st Principle Component of Ci
• Homogeneity of Ci
• Quality of clustering , is
• Optimal partition
2,
R
argmaxjn
j i
i u xu x C
y r
, cov( , ) /x y x yr x y
2,( )i jj i
i y xx CH C r
1
( ) ( )k
k ii
P H C
H
iy
H kP
*kP
10
Variable Clustering – Visualize This!
• Optimal partition:
Given a partition:P4 = (C1, …, C4)
Centroid of Ck:1st PC of Ck
H(C1) H(C2) H(C3) H(C4)Quality of P4: 4( )PH = +++
* argmax ( )k k
k kP
P P
P
H
11
Implementation
• Theoretical optimum is computationally complex
• Agglomerative hierarchical clustering– Start with the points as individual clusters– At each step, merge the closest pair of clusters until only one
cluster left
• Result can be visualized as a dendrogram
• ClustOfVar in R
12
Simulation Output
PI2: Memory PI1: Communication
0.53
* 0.52
* 0.49
*0.46
* 1.00
*
-0.1
5*
-0.0
7*
0.81
* -0.3
0*
0.45
*-0.1
4*
+ + + + + + ++PI3: Computation
13
PIs for Baseline Kernels
14
PI1 vs PI2
• 2 distinct strata on memory– Upper – multiple node runs,
need extra memory buffers– Lower – single node runs, shared
memory• High PI2 for HPL
PI1. Communication
PI2
. Mem
ory
15
PI1 vs PI3
• Similar PI3 pattern for HPL and DGEMM– Computation intensive– HPL utilize DGEMM routine
extensively• Similar all PIs for stream &
random access
PI1. Communication
PI3
. Com
puta
tion
16
Courtesy of C.-D. Lu
17
Applications
• 9 real-world scientific applications in weather forecasting, molecular dynamics and quantum physics– Amber: molecular dynamics– ExaML: molecular sequencing– GADGET: cosmology – Gromacs: molecular dynamics– HOMME: climate modeling– LAMMPS: molecular dynamics– MILC: quantum chromodynamics– NAMD: molecular dynamics– WRF: weather research
Voronoi Diagram
PI1. Communication
PI3
. Com
puta
tion
18
Conclusion and Future Work
We have
• Proposed a statistical approach to give users a better insights into massive performance datasets;
• Created a performance scoring system using 3 PIs to capture high-dimensional performance space;
• Gave user accessible performance implications and improvement hints.
We will
• Test the method on other machine and systems;
• Define and develop a set of baseline kernels that better represent HPC workloads;
• Construct a user-friendly system incorporating statistical techniques to drive more advanced performance analysis for non-experts.
19
Thanks for your attention!Questions?