automatic performance tuning and machine learning
play

Automatic Performance Tuning and Machine Learning Markus Pschel - PowerPoint PPT Presentation

Jan 13, 2023 •8 likes •388 views

Carnegie Mellon Automatic Performance Tuning and Machine Learning Markus Pschel Computer Science, ETH Zrich with: Frdric de Mesmay PhD, Electrical and Computer Engineering, Carnegie Mellon M arkus Pschel, ETH Zurich, 201 1

  1. Carnegie Mellon Automatic Performance Tuning and Machine Learning Markus Püschel Computer Science, ETH Zürich with: Frédéric de Mesmay PhD, Electrical and Computer Engineering, Carnegie Mellon M arkus Püschel, ETH Zurich, 201 1

  2. Carnegie Mellon PhD and Postdoc openings: High performance computing  Compilers  Theory  Programming languages/Generative programming  M arkus Püschel, ETH Zurich, 201 1

  3. Carnegie Mellon Why Autotuning? Matrix-Matrix Multiplication (MMM) on quadcore Intel platform Performance [Gflop/s] 50 45 40 Best implementation 35 30 25 160x 20 15 10 5 Triple loop 0 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 matrix size  Same (mathematical) operation count (2n 3 )  Compiler underperforms by 160x M arkus Püschel, ETH Zurich, 201 1

  4. Carnegie Mellon Same for All Critical Compute Functions WiFi Receiver (Physical layer) on one Intel Core Throughput [Mbit/s] vs. Data rate [Mbit/s] 35x 30x M arkus Püschel, ETH Zurich, 201 1

  5. Carnegie Mellon Solution: Autotuning Definition: Search over alternative implementations or parameters to find the fastest. Definition: Automating performance optimization with tools that complement/aid the compiler or programmer. However: Search is an important tool. But expensive. Solution: Machine learning M arkus Püschel, ETH Zurich, 201 1

  6. Carnegie Mellon Organization  Autotuning examples  An example use of machine learning M arkus Püschel, ETH Zurich, 201 1

  7. Carnegie Mellon time of time of time of implementation installation use platform known problem parameters known M arkus Püschel, ETH Zurich, 201 1

  8. Carnegie Mellon PhiPac/ATLAS: MMM Generator Whaley, Bilmes, Demmel, Dongarra , … c a b = * Blocking improves locality c = (double *) calloc(sizeof(double), n*n); /* Multiply n x n matrices a and b */ void mmm(double *a, double *b, double *c, int n) { int i, j, k; for (i = 0; i < n; i+=B) for (j = 0; j < n; j+=B) for (k = 0; k < n; k+=B) /* B x B mini matrix multiplications */ for (i1 = i; i1 < i+B; i++) for (j1 = j; j1 < j+B; j++) for (k1 = k; k1 < k+B; k++) c[i1*n+j1] += a[i1*n + k1]*b[k1*n + j1]; } M arkus Püschel, ETH Zurich, 201 1

  9. Carnegie Mellon PhiPac/ATLAS: MMM Generator Compile Mflop/s Execute Measure L1Size NB MU,NU,KU Detect MiniMMM ATLAS ATLAS MMM NR xFetch Hardware Source Search Engine Code Generator MulAdd MulAdd Parameters L * Latency source: Pingali, Yotov, Cornell U. M arkus Püschel, ETH Zurich, 201 1

  10. Carnegie Mellon ATLAS MMM generator time of time of time of implementation installation use platform known problem parameters known M arkus Püschel, ETH Zurich, 201 1

  11. Carnegie Mellon FFTW: Discrete Fourier Transform (DFT) Frigo, Johnson Installation n = 1024 configure/make radix 16 16 64 base case radix 8 Usage Twiddles Search for fastest 8 8 d = dft(n) base case base case computation strategy d(x,y) M arkus Püschel, ETH Zurich, 201 1

  12. Carnegie Mellon FFTW: Codelet Generator Frigo n DFT codelet generator dft_n (*x, *y, …) fixed size DFT function straightline code M arkus Püschel, ETH Zurich, 201 1

  13. Carnegie Mellon FFTW codelet ATLAS FFTW adaptive generator MMM generator library time of time of time of implementation installation use platform known problem parameters known M arkus Püschel, ETH Zurich, 201 1

  14. Carnegie Mellon OSKI: Sparse Matrix-Vector Multiplication Vuduc, Im, Yelick, Demmel = *  Blocking for registers:  Improves locality (reuse of input vector)  But creates overhead (zeros in block) = * M arkus Püschel, ETH Zurich, 201 1

  15. Carnegie Mellon OSKI: Sparse Matrix-Vector Multiplication Gain by blocking (dense MVM) Overhead by blocking = * 16/9 = 1.77 1.4 1.4/1.77 = 0.79 (no gain) M arkus Püschel, ETH Zurich, 201 1

  16. Carnegie Mellon OSKI OSKI sparse MVM sparse MVM FFTW codelet ATLAS FFTW adaptive generator MMM generator library time of time of time of implementation installation use platform known problem parameters known M arkus Püschel, ETH Zurich, 201 1

  17. Carnegie Mellon Spiral: Linear Transforms & More Algorithm knowledge Platform description Spiral Optimized implementation regenerated for every new platform M arkus Püschel, ETH Zurich, 201 1

  18. Carnegie Mellon Program Generation in Spiral (Sketched) Transform Optimization at all user specified abstraction levels Algorithm rules Fast algorithm parallelization in SPL vectorization many choices loop Σ -SPL optimizations constant folding Optimized implementation scheduling …… + search M arkus Püschel, ETH Zurich, 201 1

  19. Carnegie Mellon Machine learning Machine learning Spiral: transforms Spiral: transforms general input size general input size Spiral: transforms fixed input size OSKI OSKI sparse MVM sparse MVM FFTW codelet ATLAS FFTW adaptive generator MMM generator library time of time of time of implementation installation use platform known problem parameters known M arkus Püschel, ETH Zurich, 201 1

  20. Carnegie Mellon Organization  Autotuning examples  An example use of machine learning M arkus Püschel, ETH Zurich, 201 1

  21. Carnegie Mellon Online tuning Offline tuning (time of use) (time of installation) Installation Installation configure/make configure/make for a few n: search learn decision trees Use Use Twiddles Search for fastest Twiddles d = dft(n) d = dft(n) computation strategy d(x,y) d(x,y) Goal M arkus Püschel, ETH Zurich, 201 1

  22. Carnegie Mellon Integration with Spiral-Generated Libraries Voronenko 2008 Online tunable Spiral + some platform information library M arkus Püschel, ETH Zurich, 201 1

  23. Carnegie Mellon Organization  Autotuning examples  An example use of machine learning  Anatomy of an adaptive discrete Fourier transform library  Decision tree generation using C4.5  Results M arkus Püschel, ETH Zurich, 201 1

  24. Carnegie Mellon Discrete/Fast Fourier Transform  Discrete Fourier transform (DFT):  Cooley/Tukey fast Fourier transform (FFT):  Dataflow (right to left): 16 = 4 x 4 M arkus Püschel, ETH Zurich, 201 1

  25. Carnegie Mellon Adaptive Scalar Implementation (FFTW 2.x) void dft( int n, cpx *y, cpx *x) { if (use_dft_base_case(n)) Choices used for dft_bc(n, y, x); adaptation else { int k = choose_dft_radix(n); for ( int i=0; i < k; ++i) dft_strided(m, k, t + m*i, x + m*i); for ( int i=0; i < m; ++i) dft_scaled(k, m, precomp_d[i], y + i, t + i); } } void dft_strided( int n, int istr, cpx *y, cpx *x) { ... } void dft_scaled( int n, int str, cpx *d, cpx *y, cpx *x) { ... } M arkus Püschel, ETH Zurich, 201 1

  26. Carnegie Mellon Decision Graph of Library void dft( int n, cpx *y, cpx *x) { if (use_dft_base_case(n)) Choices used for dft_bc(n, y, x); adaptation else { int k = choose_dft_radix(n); for ( int i=0; i < k; ++i) dft_strided(m, k, t + m*i, x + m*i); for ( int i=0; i < m; ++i) dft_scaled(k, m, precomp_d[i], y + i, t + i); } } void dft_strided( int n, int istr, cpx *y, cpx *x) { ... } void dft_scaled( int n, int str, cpx *d, cpx *y, cpx *x) { ... } M arkus Püschel, ETH Zurich, 201 1

  27. Carnegie Mellon Spiral-Generated Libraries Standard Scalar Spiral Vectorized Threading Buffering 20 mutually recursive functions  10 different choices (occurring recursively)  Choices are heterogeneous (radix, threading, buffering, …)  M arkus Püschel, ETH Zurich, 201 1

  28. Carnegie Mellon Our Work Upon installation, generate decision trees for each choice Example: M arkus Püschel, ETH Zurich, 201 1

  29. Carnegie Mellon Statistical Classification: C4.5 Features (events) C4.5 Entropy of Features P(play|windy=false) = 6/8 H(windy=false) = 0.81 P(don’t play|windy=false) = 2/8 H(windy) = 0.89 P(play|windy=true) = 1/2 H(windy=true) = 1.0 H(outlook) = 0.69 P(don’t play|windy=false) = 1/2 H(humidity) = … M arkus Püschel, ETH Zurich, 201 1

  30. Carnegie Mellon Application to Libraries  Features = arguments of functions (except variable pointers) dft( int n, cpx *y, cpx *x) dft_strided( int n, int istr, cpx *y, cpx *x) dft_scaled( int n, int str, cpx *d, cpx *y, cpx *x)  At installation time:  Run search for a few input sizes n  Yields training set: features and associated decisions (several for each size)  Generate decision trees using C4.5 and insert into library M arkus Püschel, ETH Zurich, 201 1

  31. Carnegie Mellon Issues  Correctness of generated decision trees  Issue: learning sizes n in {12, 18, 24, 48}, may find radix 6  Solution: correction pass through decision tree  Prime factor structure n = 2 i 3 j = 2, 3, 4, 6, 9, 12, 16, 18, 24, 27, 32, … i Compute i, j and add to features j M arkus Püschel, ETH Zurich, 201 1

  32. Carnegie Mellon Experimental Setup  3GHz Intel Xeon 5160 (2 Core 2 Duos = 4 cores)  Linux 64-bit, icc 10.1  Libraries:  IPP 5.3  FFTW 3.2 alpha 2  Spiral-generated library M arkus Püschel, ETH Zurich, 201 1

  33. Carnegie Mellon Learning works as expected M arkus Püschel, ETH Zurich, 201 1

  34. Carnegie Mellon “All” Sizes  All sizes n ≤ 2 18 , with prime factors ≤ 19 M arkus Püschel, ETH Zurich, 201 1

Recommend

Automatic Database Management System Tuning Through Large-scale Machine Learning Dana Van Aken

Automatic Database Management System Tuning Through Large-scale Machine Learning Dana Van Aken

Automatic Database Management System Tuning Through Large-scale Machine Learning Dana Van Aken Andrew Pavlo Geoffrey J. Gordon Bohan Zhang 1 Weakness in manually DBMS tuning Dependencies between different knobs Changing of

1.12k views • 22 slides
Active Learning-based Automatic Tuning and Prediction of Parallel I/O Performance Megha Agarwal

Active Learning-based Automatic Tuning and Prediction of Parallel I/O Performance Megha Agarwal

Active Learning-based Automatic Tuning and Prediction of Parallel I/O Performance Megha Agarwal Divyansh Singhvi Preeti Malakar Suren Byna Indian Institute of Technology Kanpur, India PDSW @ SC'19 Lawrence Berkeley Laboratory, USA November

759 views • 27 slides
Automatic Database Management System Tuning Through Large-scale Machine Learning Van Aken Dana et

Automatic Database Management System Tuning Through Large-scale Machine Learning Van Aken Dana et

Automatic Database Management System Tuning Through Large-scale Machine Learning Van Aken Dana et al. [1] LSDPO (2017/2018) Paper Presentation: Ioana Bica (ib354) Standard approach: employ a database administrator (DBA) to tweak knobs

1.15k views • 38 slides
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using

Performance evaluation and hyperparameter tuning of statistical and machine-learning models using

Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data Patrick Schratz 1 , Jannes Muenchow 1 , Eugenia Iturritxa 2 , Jakob Richter 3 , Alexander Brenning 1 September 27 2018, 10th

632 views • 37 slides
A Hands-On Introduction to Automatic Machine Learning Lars Kotthofg University of Wyoming

A Hands-On Introduction to Automatic Machine Learning Lars Kotthofg University of Wyoming

A Hands-On Introduction to Automatic Machine Learning Lars Kotthofg University of Wyoming larsko@uwyo.edu AutoML Workshop, 28 August 2018, Nanjing 1 Machine Learning Data Machine Learning Predictions 2 Automatic Machine Learning

744 views • 33 slides
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using

Performance evaluation and hyperparameter tuning of statistical and machine-learning models using

Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data P a tr ic k S ch r a tz 1 , J a nn e s M u e n ch ow 1 , J a ko b R ich t e r 2 , A l e x a n de r B r e nn i n g 1 GIS cie n ce S e m i

688 views • 32 slides
Introduction to Machine Learning Hyperparameter Tuning - Problem Definition

Introduction to Machine Learning Hyperparameter Tuning - Problem Definition

Introduction to Machine Learning Hyperparameter Tuning - Problem Definition compstat-lmu.github.io/lecture_i2ml TUNING Recall: Hyperparameters are parameters that are inputs to the training problem, in which a learner I minimizes the

504 views • 7 slides
Automatic Performance Tuning and Analysis of Sparse Triangular Solve Richard Vuduc, Shoaib Kamil,

Automatic Performance Tuning and Analysis of Sparse Triangular Solve Richard Vuduc, Shoaib Kamil,

Automatic Performance Tuning and Analysis of Sparse Triangular Solve Richard Vuduc, Shoaib Kamil, Jen Hsu, Rajesh Nishtala James W. Demmel, Katherine A. Yelick June 22, 2002 Berkeley Benchmarking and OPtimization (BeBOP) Project

868 views • 33 slides
Machine learning with mlr Dr. Shirin Elsinghorst Data Scientist DataCamp Hyperparameter Tuning

Machine learning with mlr Dr. Shirin Elsinghorst Data Scientist DataCamp Hyperparameter Tuning

DataCamp Hyperparameter Tuning in R HYPERPARAMETER TUNING IN R Machine learning with mlr Dr. Shirin Elsinghorst Data Scientist DataCamp Hyperparameter Tuning in R The mlr package mlr is another framework for machine learning in R. Model

454 views • 28 slides
Introduction to Machine Learning Tuning: Nested Resampling compstat-lmu.github.io/lecture_i2ml

Introduction to Machine Learning Tuning: Nested Resampling compstat-lmu.github.io/lecture_i2ml

Introduction to Machine Learning Tuning: Nested Resampling compstat-lmu.github.io/lecture_i2ml NESTED RESAMPLING Just like we can generalize holdout splitting to resampling to get more reliable estimates of the predictive performance, we can

545 views • 7 slides
CAPES:Unsupervised Storage Performance Tuning Using Neural Network-Based Deep Reinforcement

CAPES:Unsupervised Storage Performance Tuning Using Neural Network-Based Deep Reinforcement

CAPES:Unsupervised Storage Performance Tuning Using Neural Network-Based Deep Reinforcement Learning Yan li, Kenneth Chang, Oceane Bel, Ethan L. Miller, Darrel D. E. Long Performance Tuning Tuning systems parameters for high

343 views • 15 slides
Introduction to Machine Learning Hyperparameter Tuning - Introduction

Introduction to Machine Learning Hyperparameter Tuning - Introduction

Introduction to Machine Learning Hyperparameter Tuning - Introduction compstat-lmu.github.io/lecture_i2ml MOTIVATING EXAMPLE Given a dataset, we want to train a classification tree. We feel that a maximum tree depth of 4 has worked out well for

499 views • 7 slides
Machine learning with H2O Dr. Shirin Glander Data Scientist DataCamp Hyperparameter Tuning in R

Machine learning with H2O Dr. Shirin Glander Data Scientist DataCamp Hyperparameter Tuning in R

DataCamp Hyperparameter Tuning in R HYPERPARAMETER TUNING IN R Machine learning with H2O Dr. Shirin Glander Data Scientist DataCamp Hyperparameter Tuning in R What is H2O? library(h2o) h2o.init() H2O is not running yet, starting it now...

773 views • 30 slides
Performance Models for Evaluation and Automatic Tuning of Symmetric Sparse Matrix-Vector Multiply

Performance Models for Evaluation and Automatic Tuning of Symmetric Sparse Matrix-Vector Multiply

Performance Models for Evaluation and Automatic Tuning of Symmetric Sparse Matrix-Vector Multiply University of California, Berkeley Berkeley Benchmarking and Optimization Group (BeBOP) http://bebop.cs.berkeley.edu Benjamin C. Lee, Richard W.

691 views • 32 slides
Introduction to Machine Learning Tuning: Nested Resampling Motivation

Introduction to Machine Learning Tuning: Nested Resampling Motivation

Introduction to Machine Learning Tuning: Nested Resampling Motivation compstat-lmu.github.io/lecture_i2ml MOTIVATION Selecting the best model from a set of potential candidates (e.g., different classes of learners, different hyperparameter

662 views • 9 slides
Introduction to Machine Learning Hyperparameter Tuning - Basic Techniques

Introduction to Machine Learning Hyperparameter Tuning - Basic Techniques

Introduction to Machine Learning Hyperparameter Tuning - Basic Techniques compstat-lmu.github.io/lecture_i2ml GRID SEARCH Simple technique which is still quite popular, tries all HP combinations on a multi-dimensional discretized grid For

1.06k views • 7 slides
Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 12, 2015 Today: Readings: What is machine learning? The Discipline of ML Decision tree learning Mitchell, Chapter 3

872 views • 29 slides
Statistical Models for Automatic Performance Tuning Richard Vuduc, James Demmel (U.C. Berkeley,

Statistical Models for Automatic Performance Tuning Richard Vuduc, James Demmel (U.C. Berkeley,

Statistical Models for Automatic Performance Tuning Richard Vuduc, James Demmel (U.C. Berkeley, EECS) {richie,demmel}@cs.berkeley.edu Jeff Bilmes (Univ. of Washington, EE) bilmes@ee.washington.edu May 29, 2001 International Conference on

617 views • 51 slides
PAC PACE AUT AUTO-WER WERKS KS Vehicle Tuning Services Performance tuning with fuel

PAC PACE AUT AUTO-WER WERKS KS Vehicle Tuning Services Performance tuning with fuel

PAC PACE AUT AUTO-WER WERKS KS Vehicle Tuning Services Performance tuning with fuel efficiency Com pany Profile Viezu specialize in electronic engine tuning, rolling roads, diagnostic and test equipment Dynocom EU launched 2010;

281 views • 15 slides
1 Why Study Machine Learning? Why Study Machine Learning? Cognitive Science The Time is Ripe

1 Why Study Machine Learning? Why Study Machine Learning? Cognitive Science The Time is Ripe

What is Learning? Herbert Simon: Learning is any process by which a system improves performance from CS 391L: Machine Learning experience. Introduction What is the task? Classification Problem solving / planning /

603 views • 6 slides
Machine Learning Machine Learning: algorithms that use experience to improve their

Machine Learning Machine Learning: algorithms that use experience to improve their

Machine Learning Machine Learning: algorithms that use experience to improve their performance We use machine learning in situations where it is very challenging (or impossible) to define the rules by hand: e.g. face detection

1.04k views • 28 slides
The tension between convenience and performance in automatic differentiation Jeffrey Mark Siskind,

The tension between convenience and performance in automatic differentiation Jeffrey Mark Siskind,

The tension between convenience and performance in automatic differentiation Jeffrey Mark Siskind, qobi@purdue.edu NIPS 2016 Workshop on The Future of Gradient-Based Machine Learning Software Saturday 10 December 2016 Joint work with Barak

2.07k views • 184 slides
Scikit-learn 1 / 13 Machine Learning Learning: using experience to improve performance.

Scikit-learn 1 / 13 Machine Learning Learning: using experience to improve performance.

Scikit-learn 1 / 13 Machine Learning Learning: using experience to improve performance. Machine learning: a class of algorithms that uses data (experience) to improve performance on a task Kinds of Tasks Classification: identify the

568 views • 13 slides
Performance Tuning best pracitces and performance monitoring with Zabbix Andrew Nelson Senior

Performance Tuning best pracitces and performance monitoring with Zabbix Andrew Nelson Senior

Performance Tuning best pracitces and performance monitoring with Zabbix Andrew Nelson Senior Linux Consultant May 28, 2015 NLUUG Conf, Utrecht, Netherlands Overview Introduction Performance tuning is Science! A little Law and

595 views • 47 slides

More recommend