This paper describes the automatically tuned linear algebra software (ATLAS) project, as well as the fundamental principles that underly it. ATLAS is an instantiation of a new paradigm in high performance library production and maintenance, which we term automated empirical optimization of software (AEOS); this style of library management has been created in order to allow software to keep pace with the incredible rate of hardware advancement inherent in Moore's Law. ATLAS is the application of this new paradigm to linear algebra software, with the present emphasis on the basic linear algebra subprograms (BLAS), a widely used, performance-critical, linear algebra kernel library.

/pdf/new-trends-in-high-performance-computing-1f0278f9m2.pdf

New trends in high performance computing

Aimed at the working researcher or scientific C/C++ or Fortran programmer, this text introduces the competent research programmer to a new vocabulary of idioms and techniques for parallelizing software using OpenMP.

http://www.dfmf.uned.es/~osotolongo/pub/mpi/Parallel%20programming%20in%20OpenMP.pdf

Parallel Programming in OpenMP

Social science research contains a wealth of knowledge for people seeking to understand collaboration processes. The authors argue that public managers should look inside the “black box” of collaboration processes. Inside, they will find a complex construct of five variable dimensions: governance, administration, organizational autonomy, mutuality, and norms. Public managers must know these five dimensions and manage them intentionally in order to collaborate effectively.

https://www.jstor.org/stable/pdfplus/4096567.pdf

Collaboration Processes: Inside the Black Box

It has become more and more common to distribute software in forms that retain most or all of the information present in the original source code. An important example is Java bytecode. Since such codes are easy to decompile, they increase the risk of malicious reverse engineering attacks. In this paper we review several techniques for technical protection of software secrets. We will argue that automatic code obfuscation is currently the most viable method for preventing reverse engineering. We then describe the design of a code obfuscator, a tool which converts a program into an equivalent one that is more diicult to understand and reverse engineer. The obfuscator is based on the application of code transformations, in many cases similar to those used by compiler optimizers. We describe a large number of such transformations, classify them, and evaluate them with respect to their potency (To what degree is a human reader confused?), resilience (How well are automatic deobfuscation attacks resisted?), and cost (How much overhead is added to the application?). We nally discuss some possible deobfuscation techniques (such as program slicing) and possible countermeasures an obfuscator could employ against them.

A Taxonomy of Obfuscating Transformations

This paper describes the ATLAS (Automatically Tuned Linear Algebra Software) project, as well as the fundamental principles that underly it. ATLAS is an instantiation of a new paradigm in high performance library production and maintenance, which we term AEOS (Automated Empirical Optimization of Software); this style of library management has been created in order to allow software to keep pace with the incredible rate of hardware advancement inherent in Moore's Law. ATLAS is the application of this new paradigm to linear algebra software, with the present emphasis on the Basic Linear Algebra Subprograms (BLAS), a widely used, performance-critical, linear algebra kernel library. This work was supported in part by: U.S. Department of Energy under contract number DE-AC0596OR22464; National Science Foundation Science and Technology Center Cooperative Agreement No. CCR-8809615; University of California, Los Alamos National Laboratory, subcontract # B76680017-3Z; Department of Defense Raytheon E-Systems, subcontract# AA23, prime contract# DAHC94-96-C-0010; Department of Defense Nichols Research Corporation, subcontract#s NRC CR-96-0011 (ASC) and prime contract # DAHC-94-96-C-0005; Department of Defense Nichols Research Corporation, subcontract#s NRC CR-96-0011 (CEWES); prime contract # DAHC-94-96-C-0002 Dept. of Computer Sciences, Univ. of TN, Knoxville, TN 37996, rwhaley@cs.utk.edu Dept. of Computer Sciences, Univ. of TN, Knoxville, TN 37996, petitet@cs.utk.edu Dept. of Computer Sciences, Univ. of TN, Knoxville, TN 37996, and Mathematical Sciences Section, ORNL, Oak Ridge, TN 37831, dongarra@cs.utk.edu

/pdf/automated-empirical-optimizations-of-software-and-the-atlas-3wpoadext0.pdf

Automated Empirical Optimizations of Software and the ATLAS Project (LAPACK Working Note 147)

1. High Performance Systems. An Example Program: Matrix Multiplication. Structure of a Compiler. 2. Programming Language Features. Languages for High Performance. Sequential and Parallel Loops. Roundoff Error. 3. Basic Graph Concepts. Sets, Tuples, Logic. Graphs. Control Dependence. 4. Review of Linear Algebra. Real Vectors and Matrices. Integer Matrices and Lattices. Linear System of Equations. System of Integer Equations. Systems of Linear Inequalities. Systems of Integer Linear Inequalities. Extreme Values of Affine Functions. 5. Data Dependence. Data Dependence in Loops. Data Dependence in Conditionals. Data Dependence in Parallel Loops. Program Dependence Graph. 6. Scalar Analysis with Factored Use-Def Chains. Constructing Factored Use-Def Chains. FUD Chains for Arrays. Finding All Reaching Definitions. Implicit References in FUD Chains. InductionVariables Using FUD Chains. Constant Propagation with FUD Chains. Data Dependence for Scalars. 7. Data Dependence Analysis for Arrays. Building the Dependence System. Dependence System Solvers. General Solver. Summary of Solvers. Complications. Run-time Dependence Testing. 8. Other Dependence Problems. Array Region Analysis. Pointer Analysis. I/O Dependence. Procedure Calls. Interprocedural Analysis. 9. Loop Restructuring. Simpile Transformations. Loop Fusion. Loop Fission. Loop Reversal. Loop Interchanging. Loop Skewing. Linear Loop Transformations. Strip-Mining. Loop Tiling. Other Loop Transformations. Interprocedural Transformations. 10. Optimizing for Locality. Single Reference to Each Array. Multiple References. General Tiling. Fission and Fusion for Locality. 11. Concurrency Analysis. Code for Concurrent Loops. Concurrency from Sequential Loops. Concurrency from Parallel Loops. Nested Loops. Roundoff Error. Exceptions and Debuggers. 12. Vector Analysis. Vector Code. Vector Code from Sequential Loops. Vector Code from Forall Loops. Nested Loops. Roundoff Error, Exceptions, and Debuggers. Multivector Computers. 13. Message-Passing Machines. SIMD Machines. MIMD Machines. Data Layout. Parallel Code for Array Assignment. Remote Data Access. Automatic Data Layout. Multiple Array Assignments. Other Topics. 14. Scalable Shared-Memory Machines. Global Cache Coherence. Local Cache Coherence. Latency Tolerant Machines. Glossary. References. Author Index. Index. 0805327304T04062001

High-Performance Compilers for Parallel Computing

The goal of this course, and hence of this book, is to expose first-year grad- uate students to a wide range of programming language paradigms and is- sues, so that they can understand the literature on programming languages and even conduct research in this field.

/pdf/advanced-programming-language-design-c96kxdcjuk.pdf

Leda Ortega

Papers

High-Performance Compilers for Parallel Computing

Advanced Programming Language Design