statistical performance analysis for scientific applications

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.

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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

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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

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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

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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

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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

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PIs for Baseline Kernels

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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

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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

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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.

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Thanks for your attention!Questions?