Using Machine Learning to Improve Dense and Sparse Matrix Multiplication Kernels
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Linpack Evaluation on a Supercomputer with Heterogeneous Accelerators
Linpack Evaluation on a Supercomputer with Heterogeneous Accelerators Toshio Endo Akira Nukada Graduate School of Information Science and Engineering Global Scientific Information and Computing Center Tokyo Institute of Technology Tokyo Institute of Technology Tokyo, Japan Tokyo, Japan [email protected] [email protected] Satoshi Matsuoka Naoya Maruyama Global Scientific Information and Computing Center Global Scientific Information and Computing Center Tokyo Institute of Technology/National Institute of Informatics Tokyo Institute of Technology Tokyo, Japan Tokyo, Japan [email protected] [email protected] Abstract—We report Linpack benchmark results on the Roadrunner or other systems described above, it includes TSUBAME supercomputer, a large scale heterogeneous system two types of accelerators. This is due to incremental upgrade equipped with NVIDIA Tesla GPUs and ClearSpeed SIMD of the system, which has been the case in commodity CPU accelerators. With all of 10,480 Opteron cores, 640 Xeon cores, 648 ClearSpeed accelerators and 624 NVIDIA Tesla GPUs, clusters; they may have processors with different speeds as we have achieved 87.01TFlops, which is the third record as a result of incremental upgrade. In this paper, we present a heterogeneous system in the world. This paper describes a Linpack implementation and evaluation results on TSUB- careful tuning and load balancing method required to achieve AME with 10,480 Opteron cores, 624 Tesla GPUs and 648 this performance. On the other hand, since the peak speed is ClearSpeed accelerators. In the evaluation, we also used a 163 TFlops, the efficiency is 53%, which is lower than other systems. -
Data Structure
EDUSAT LEARNING RESOURCE MATERIAL ON DATA STRUCTURE (For 3rd Semester CSE & IT) Contributors : 1. Er. Subhanga Kishore Das, Sr. Lect CSE 2. Mrs. Pranati Pattanaik, Lect CSE 3. Mrs. Swetalina Das, Lect CA 4. Mrs Manisha Rath, Lect CA 5. Er. Dillip Kumar Mishra, Lect 6. Ms. Supriti Mohapatra, Lect 7. Ms Soma Paikaray, Lect Copy Right DTE&T,Odisha Page 1 Data Structure (Syllabus) Semester & Branch: 3rd sem CSE/IT Teachers Assessment : 10 Marks Theory: 4 Periods per Week Class Test : 20 Marks Total Periods: 60 Periods per Semester End Semester Exam : 70 Marks Examination: 3 Hours TOTAL MARKS : 100 Marks Objective : The effectiveness of implementation of any application in computer mainly depends on the that how effectively its information can be stored in the computer. For this purpose various -structures are used. This paper will expose the students to various fundamentals structures arrays, stacks, queues, trees etc. It will also expose the students to some fundamental, I/0 manipulation techniques like sorting, searching etc 1.0 INTRODUCTION: 04 1.1 Explain Data, Information, data types 1.2 Define data structure & Explain different operations 1.3 Explain Abstract data types 1.4 Discuss Algorithm & its complexity 1.5 Explain Time, space tradeoff 2.0 STRING PROCESSING 03 2.1 Explain Basic Terminology, Storing Strings 2.2 State Character Data Type, 2.3 Discuss String Operations 3.0 ARRAYS 07 3.1 Give Introduction about array, 3.2 Discuss Linear arrays, representation of linear array In memory 3.3 Explain traversing linear arrays, inserting & deleting elements 3.4 Discuss multidimensional arrays, representation of two dimensional arrays in memory (row major order & column major order), and pointers 3.5 Explain sparse matrices. -
Fast Matrix Multiplication
Fast matrix multiplication Yuval Filmus May 3, 2017 1 Problem statement How fast can we multiply two square matrices? To make this problem precise, we need to fix a computational model. The model that we use is the unit cost arithmetic model. In this model, a program is a sequence of instructions, each of which is of one of the following forms: 1. Load input: tn xi, where xi is one of the inputs. 2. Load constant: tn c, where c 2 R. 3. Arithmetic operation: tn ti ◦ tj, where i; j < n, and ◦ 2 f+; −; ·; =g. In all cases, n is the number of the instruction. We will say that an arithmetic program computes the product of two n × n matrices if: 1. Its inputs are xij; yij for 1 ≤ i; j ≤ n. 2. For each 1 ≤ i; k ≤ n there is a variable tm whose value is always equal to n X xijyjk: j=1 3. The program never divides by zero. The cost of multiplying two n × n matrices is the minimum length of an arithmetic program that computes the product of two n × n matrices. Here is an example. The following program computes the product of two 1 × 1 matrices: t1 x11 t2 y11 t3 t1 · t2 The variable t3 contains x11y11, and so satisfies the second requirement above for (i; k) = (1; 1). This shows that the cost of multiplying two 1 × 1 matrices is at most 3. Usually we will not spell out all steps of an arithmetic program, and use simple shortcuts, such as the ones indicated by the following program for multiplying two 2 × 2 matrices: z11 x11y11 + x12y21 z12 x11y12 + x12y22 z21 x21y11 + x22y21 z22 x21y12 + x22y22 1 When spelled out, this program uses 20 instructions: 8 load instructions, 8 product instructions, and 4 addition instructions. -
Easybuild Documentation Release 20210907.0
EasyBuild Documentation Release 20210907.0 Ghent University Tue, 07 Sep 2021 08:55:41 Contents 1 What is EasyBuild? 3 2 Concepts and terminology 5 2.1 EasyBuild framework..........................................5 2.2 Easyblocks................................................6 2.3 Toolchains................................................7 2.3.1 system toolchain.......................................7 2.3.2 dummy toolchain (DEPRECATED) ..............................7 2.3.3 Common toolchains.......................................7 2.4 Easyconfig files..............................................7 2.5 Extensions................................................8 3 Typical workflow example: building and installing WRF9 3.1 Searching for available easyconfigs files.................................9 3.2 Getting an overview of planned installations.............................. 10 3.3 Installing a software stack........................................ 11 4 Getting started 13 4.1 Installing EasyBuild........................................... 13 4.1.1 Requirements.......................................... 14 4.1.2 Using pip to Install EasyBuild................................. 14 4.1.3 Installing EasyBuild with EasyBuild.............................. 17 4.1.4 Dependencies.......................................... 19 4.1.5 Sources............................................. 21 4.1.6 In case of installation issues. .................................. 22 4.2 Configuring EasyBuild.......................................... 22 4.2.1 Supported configuration -
Efficient Matrix Multiplication on Simd Computers* P
SIAM J. MATRIX ANAL. APPL. () 1992 Society for Industrial and Applied Mathematics Vol. 13, No. 1, pp. 386-401, January 1992 025 EFFICIENT MATRIX MULTIPLICATION ON SIMD COMPUTERS* P. BJORSTADt, F. MANNEr, T. SOI:tEVIK, AND M. VAJTERIC?$ Dedicated to Gene H. Golub on the occasion of his 60th birthday Abstract. Efficient algorithms are described for matrix multiplication on SIMD computers. SIMD implementations of Winograd's algorithm are considered in the case where additions are faster than multiplications. Classical kernels and the use of Strassen's algorithm are also considered. Actual performance figures using the MasPar family of SIMD computers are presented and discussed. Key words, matrix multiplication, Winograd's algorithm, Strassen's algorithm, SIMD com- puter, parallel computing AMS(MOS) subject classifications. 15-04, 65-04, 65F05, 65F30, 65F35, 68-04 1. Introduction. One of the basic computational kernels in many linear algebra codes is the multiplication of two matrices. It has been realized that most problems in computational linear algebra can be expressed in block algorithms and that matrix multiplication is the most important kernel in such a framework. This approach is essential in order to achieve good performance on computer systems having a hier- archical memory organization. Currently, computer technology is strongly directed towards this design, due to the imbalance between the rapid advancement in processor speed relative to the much slower progress towards large, inexpensive, fast memories. This algorithmic development is highlighted by Golub and Van Loan [13], where the formulation of block algorithms is an integrated part of the text. The rapid devel- opment within the field of parallel computing and the increased need for cache and multilevel memory systems are indeed well reflected in the changes from the first to the second edition of this excellent text and reference book. -
Matrix Multiplication: a Study Kristi Stefa Introduction
Matrix Multiplication: A Study Kristi Stefa Introduction Task is to handle matrix multiplication in an efficient way, namely for large sizes Want to measure how parallelism can be used to improve performance while keeping accuracy Also need for improvement on “normal” multiplication algorithm O(n3) for conventional square matrices of n x n size Strassen Algorithm Introduction of a different algorithm, one where work is done on submatrices Requirement of matrix being square and size n = 2k Uses A and B matrices to construct 7 factors to solve C matrix Complexity from 8 multiplications to 7 + additions: O(nlog27) or n2.80 Room for parallelism TBB:parallel_for is the perfect candidate for both the conventional algorithm and the Strassen algorithm Utilization in ranges of 1D and 3D to sweep a vector or a matrix TBB: parallel_reduce would be possible but would increase the complexity of usage Would be used in tandem with a 2D range for the task of multiplication Test Setup and Use Cases The input images were converted to binary by Matlab and the test filter (“B” matrix) was generated in Matlab and on the board Cases were 128x128, 256x256, 512x512, 1024x1024 Matlab filter was designed for element-wise multiplication and board code generated for proper matrix-wise filter Results were verified by Matlab and an import of the .bof file Test setup (contd) Pictured to the right are the results for the small (128x128) case and a printout for the 256x256 case Filter degrades the image horizontally and successful implementation produces -
Data Structure Invariants
Lecture 8 Notes Data Structures 15-122: Principles of Imperative Computation (Spring 2016) Frank Pfenning, André Platzer, Rob Simmons 1 Introduction In this lecture we introduce the idea of imperative data structures. So far, the only interfaces we’ve used carefully are pixels and string bundles. Both of these interfaces had the property that, once we created a pixel or a string bundle, we weren’t interested in changing its contents. In this lecture, we’ll talk about an interface that mimics the arrays that are primitively available in C0. To implement this interface, we’ll need to round out our discussion of types in C0 by discussing pointers and structs, two great tastes that go great together. We will discuss using contracts to ensure that pointer accesses are safe. Relating this to our learning goals, we have Computational Thinking: We illustrate the power of abstraction by con- sidering both the client-side and library-side of the interface to a data structure. Algorithms and Data Structures: The abstract arrays will be one of our first examples of abstract datatypes. Programming: Introduction of structs and pointers, use and design of in- terfaces. 2 Structs So far in this course, we’ve worked with five different C0 types — int, bool, char, string, and arrays t[] (there is a array type t[] for every type t). The character, Boolean and integer values that we manipulate, store LECTURE NOTES Data Structures L8.2 locally, and pass to functions are just the values themselves. For arrays (and strings), the things we store in assignable variables or pass to functions are addresses, references to the place where the data stored in the array can be accessed. -
The Strassen Algorithm and Its Computational Complexity
Utrecht University BSc Mathematics & Applications The Strassen Algorithm and its Computational Complexity Rein Bressers Supervised by: Dr. T. van Leeuwen June 5, 2020 Abstract Due to the many computational applications of matrix multiplication, research into efficient algorithms for multiplying matrices can lead to widespread improvements of performance. In this thesis, we will first make the reader familiar with a universal measure of the efficiency of an algorithm, its computational complexity. We will then examine the Strassen algorithm, an algorithm that improves on the computational complexity of the conventional method for matrix multiplication. To illustrate the impact of this difference in complexity, we implement and test both algorithms, and compare their runtimes. Our results show that while Strassen’s method improves on the theoretical complexity of matrix multiplication, there are a number of practical considerations that need to be addressed for this to actually result in improvements on runtime. 2 Contents 1 Introduction 4 1.1 Computational complexity .................................................................................. 4 1.2 Structure ........................................................................................................... 4 2 Computational Complexity ................................................................ 5 2.1 Time complexity ................................................................................................ 5 2.2 Determining an algorithm’s complexity .............................................................. -
CMPSCI 585 Programming Assignment 1 Spam Filtering Using Naive Bayes
CMPSCI 585 Programming Assignment 1 Out: February 5, 2003 Due: February 17, 2003 Spam Filtering using Naive Bayes Naive Bayes is a simple, effective machine learning solution to the problem of document classification. For this assignment, you will implement a naive Bayes classifier to classify email messages as either spam (junk mail) or ham (legitimate messages). Data The data was collected from http://spamassassin.org/publiccorpus/. For this assignment, use the slightly modified version found at http://canberra.cs.umass.edu/~culotta/cs585/ass1-data.tgz (8.3M). The data consists of 4150 ham messages and 1897 spam messages, with original header information intact. Tasks Read data: Read in all messages and store them in some efficient manner. To do this you must decide how to tokenize each message – i.e. designate which characters indicate the start of a new “word.” See http://www.paulgrahm.com/spam.html for one way of doing this. (You will probably also assign each unique word an integer index you can use as an index into arrays of word counts and word proabilities.) Split Data: Randomly split the messages into a training set (70% of the messages) and a testing set (30%). Train Classifier: Using the training set only, estimate and store the prior class distributions P (spam) and P (ham), as well as the conditional probability distributions P (w|spam) and P (w|ham). Test Classifier: Classify each message in the testing set as spam or ham according to the Naive Bayes formulation. Note that you will most likely run out of floating-point resolution unless you do the product over words 1 Spam Ham Spam TP FP Ham FN TN Table 1: Confusion Matrix: TP = “true positive”, TN = “true negative”, FP = “false positive”, ‘FN = “false negative”. -
Supermatrix: a Multithreaded Runtime Scheduling System for Algorithms-By-Blocks
SuperMatrix: A Multithreaded Runtime Scheduling System for Algorithms-by-Blocks Ernie Chan Field G. Van Zee Paolo Bientinesi Enrique S. Quintana-Ort´ı Robert van de Geijn Department of Computer Science Gregorio Quintana-Ort´ı Department of Computer Sciences Duke University Departamento de Ingenier´ıa y Ciencia de The University of Texas at Austin Durham, NC 27708 Computadores Austin, Texas 78712 [email protected] Universidad Jaume I {echan,field,rvdg}@cs.utexas.edu 12.071–Castellon,´ Spain {quintana,gquintan}@icc.uji.es Abstract which led to the adoption of out-of-order execution in many com- This paper describes SuperMatrix, a runtime system that paral- puter microarchitectures [32]. For the dense linear algebra opera- lelizes matrix operations for SMP and/or multi-core architectures. tions on which we will concentrate in this paper, many researchers We use this system to demonstrate how code described at a high in the early days of distributed-memory computing recognized that level of abstraction can achieve high performance on such archi- “compute-ahead” techniques could be used to improve parallelism. tectures while completely hiding the parallelism from the library However, the coding complexity required of such an effort proved programmer. The key insight entails viewing matrices hierarchi- too great for these techniques to gain wide acceptance. In fact, cally, consisting of blocks that serve as units of data where oper- compute-ahead optimizations are still absent from linear algebra ations over those blocks are treated as units of computation. The packages such as ScaLAPACK [12] and PLAPACK [34]. implementation transparently enqueues the required operations, in- Recently, there has been a flurry of interest in reviving the idea ternally tracking dependencies, and then executes the operations of compute-ahead [1, 25, 31]. -
Benchmark of C++ Libraries for Sparse Matrix Computation
Benchmark of C++ Libraries for Sparse Matrix Computation Georg Holzmann http://grh.mur.at email: [email protected] August 2007 This report presents benchmarks of C++ scientific computing libraries for small and medium size sparse matrices. Be warned: these benchmarks are very special- ized on a neural network like algorithm I had to implement. However, also the initialization time of sparse matrices and a matrix-vector multiplication was mea- sured, which might be of general interest. WARNING At the time of its writing this document did not consider the eigen library (http://eigen.tuxfamily.org). Please evaluate also this very nice, fast and well maintained library before making any decisions! See http://grh.mur.at/blog/matrix-library-benchmark-follow for more information. Contents 1 Introduction 2 2 Benchmarks 2 2.1 Initialization.....................................3 2.2 Matrix-Vector Multiplication............................4 2.3 Neural Network like Operation...........................4 3 Results 4 4 Conclusion 12 A Libraries and Flags 12 B Implementations 13 1 1 Introduction Quite a lot open-source libraries exist for scientific computing, which makes it hard to choose between them. Therefore, after communication on various mailing lists, I decided to perform some benchmarks, specialized to the kind of algorithms I had to implement. Ideally algorithms should be implemented in an object oriented, reuseable way, but of course without a loss of performance. BLAS (Basic Linear Algebra Subprograms - see [1]) and LA- PACK (Linear Algebra Package - see [3]) are the standard building blocks for efficient scientific software. Highly optimized BLAS implementations for all relevant hardware architectures are available, traditionally expressed in Fortran routines for scalar, vector and matrix operations (called Level 1, 2 and 3 BLAS). -
Interfacing Epetra to the RSB Sparse Matrix Format for Shared-Memory Performance
Interfacing Epetra to the RSB sparse matrix format for shared-memory performance Michele Martone High Level Support Team Max Planck Institute for Plasma Physics Garching bei M¨unchen,Germany 5th European Trilinos User Group Meeting Garching bei M¨unchen, Germany April 19, 2016 1 / 18 Background: sparse kernels can be performance critical Sparse kernel Operation definition1 SpMV: Matrix-Vector Multiply \y β y + α A x" SpMV-T: (Transposed) \y β y + α AT x" SpSV : Triangular Solve \x α L−1 x" SpSV-T : (Transposed) \x α L−T x" Epetra_... Epetra_CrsMatrix I CRS (Compressed Row Storage) versatile and straightforward I class Epetra CrsMatrix can rely on Intel MKL 1A is a sparse matrix, L sparse triangular, x; y vectors or multi-vectors, α; β scalars. 2 / 18 Background: optimized sparse matrix classes Epetra_... Epetra_CrsMatrix Epetra_OskiMatrix I OSKI (Optimized Sparse Kernels Interface) library, by R.Vuduc I kernels for iterative methods, e.g. SpMV, SpSV I autotuning framework I OSKI + Epetra = class Epetra OskiMatrix, by I.Karlin 3 / 18 Background: librsb I a stable \Sparse BLAS"-like kernels library: I Sparse BLAS API (blas sparse.h) I own API (rsb.h) I LGPLv3 licensed I librsb-1.2 at http://librsb.sourceforge.net/ I OpenMP threaded 2 I C/C++/FORTRAN interface 2ISO-C-BINDING for own and F90 for Sparse BLAS 4 / 18 What can librsb do 3 I can be multiple times faster than Intel MKL's CRS in I symmetric/transposed sparse matrix multiply (SpMV) I multi-RHS (SpMM) I large matrices I threaded SpSV/SpSM (sparse triangular solve) I iterative methods ops: scaling, extraction, update, ..