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Ada-95: a Guide for C and C++ Programmers
Ada-95: A guide for C and C++ programmers by Simon Johnston 1995 Welcome ... to the Ada guide especially written for C and C++ programmers. Summary I have endeavered to present below a tutorial for C and C++ programmers to show them what Ada can provide and how to set about turning the knowledge and experience they have gained in C/C++ into good Ada programming. This really does expect the reader to be familiar with C/C++, although C only programmers should be able to read it OK if they skip section 3. My thanks to S. Tucker Taft for the mail that started me on this. 1 Contents 1 Ada Basics. 7 1.1 C/C++ types to Ada types. 8 1.1.1 Declaring new types and subtypes. 8 1.1.2 Simple types, Integers and Characters. 9 1.1.3 Strings. {3.6.3} ................................. 10 1.1.4 Floating {3.5.7} and Fixed {3.5.9} point. 10 1.1.5 Enumerations {3.5.1} and Ranges. 11 1.1.6 Arrays {3.6}................................... 13 1.1.7 Records {3.8}. ................................. 15 1.1.8 Access types (pointers) {3.10}.......................... 16 1.1.9 Ada advanced types and tricks. 18 1.1.10 C Unions in Ada, (food for thought). 22 1.2 C/C++ statements to Ada. 23 1.2.1 Compound Statement {5.6} ........................... 24 1.2.2 if Statement {5.3} ................................ 24 1.2.3 switch Statement {5.4} ............................. 25 1.2.4 Ada loops {5.5} ................................. 26 1.2.4.1 while Loop . -
Podcast Ch23a
Podcast Ch23a • Title: Bit Arrays • Description: Overview; bit operations in Java; BitArray class • Participants: Barry Kurtz (instructor); John Helfert and Tobie Williams (students) • Textbook: Data Structures for Java; William H. Ford and William R. Topp Bit Arrays • Applications such as compiler code generation and compression algorithms create data that includes specific sequences of bits. – Many applications, such as compilers, generate specific sequences of bits. Bit Arrays (continued) • Java binary bit handling operators |, &, and ^ act on pairs of bits and return the new value. The unary operator ~ inverts the bits of its operand. BitBit OperationsOperations x y ~x x | y x & y x ^ y 0 0 1 0 0 0 0 1 1 1 0 1 1 0 0 1 0 1 1 1 0 1 1 0 Bit Arrays (continued) Bit Arrays (continued) • Operator << shifts integer or char values to the left. Operators >> and >>> shift values to the right using signed or unsigned arithmetic, respectively. Assume x and y are 32-bit integers. x = 0...10110110 x << 2 = 0...1011011000 x = 101...11011100 x >> 3 = 111101...11011 x = 101...11011100 x >>> 3 = 000101...11011 Bit Arrays (continued) Before performing the bitwise operator |, &, or ^, Java performs binary numeric promotion on the operands. The type of the bitwise operator expression is the promoted type of the operands. The rules of promotion are as follows: • If either operand is of type long, the other is converted to long. • Otherwise, both operands are converted to type int. In the case of the unary operator ~, Java converts a byte, char, short to int before applying the operator, and the resulting value is an int. -
An Introduction to Numpy and Scipy
An introduction to Numpy and Scipy Table of contents Table of contents ............................................................................................................................ 1 Overview ......................................................................................................................................... 2 Installation ...................................................................................................................................... 2 Other resources .............................................................................................................................. 2 Importing the NumPy module ........................................................................................................ 2 Arrays .............................................................................................................................................. 3 Other ways to create arrays............................................................................................................ 7 Array mathematics .......................................................................................................................... 8 Array iteration ............................................................................................................................... 10 Basic array operations .................................................................................................................. 11 Comparison operators and value testing .................................................................................... -
Worksheet 4. Matrices in Matlab
MS6021 Scientific Computation Worksheet 4 Worksheet 4. Matrices in Matlab Creating matrices in Matlab Matlab has a number of functions for generating elementary and common matri- ces. zeros Array of zeros ones Array of ones eye Identity matrix repmat Replicate and tile array blkdiag Creates block diagonal array rand Uniformly distributed randn Normally distributed random number linspace Linearly spaced vector logspace Logarithmically spaced vector meshgrid X and Y arrays for 3D plots : Regularly spaced vector : Array slicing If given a single argument they construct square matrices. octave:1> eye(4) ans = 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 If given two entries n and m they construct an n × m matrix. octave:2> rand(2,3) ans = 0.42647 0.81781 0.74878 0.69710 0.42857 0.24610 It is also possible to construct a matrix the same size as an existing matrix. octave:3> x=ones(4,5) x = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 William Lee [email protected] 1 MS6021 Scientific Computation Worksheet 4 octave:4> y=zeros(size(x)) y = 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 • Construct a 4 by 4 matrix whose elements are random numbers evenly dis- tributed between 1 and 2. • Construct a 3 by 3 matrix whose off diagonal elements are 3 and whose diagonal elements are 2. 1001 • Construct the matrix 0101 0011 The size function returns the dimensions of a matrix, while length returns the largest of the dimensions (handy for vectors). -
Binary Index Trees a Cumulative Frequency Array Allows Us To
Binary Index Trees A cumulative frequency array allows us to calculate the sum of the range of values in O(1), as long as there are no changes to the data once the queries start. But consider situations where we might change the value at an index in an array, then query for the sum of a range of values in the array, followed by some more changes and more queries. In this sort of situation, a cumulative frequency array would still give us O(1) query times, but it would take O(n) time to update after each change!!! (Basically, if we change one index in a cumulative frequency array, all other indexes above it would have to have this value added to it as well.) Here is a quick illustration: Current cumulative frequency array: Index 0 1 2 3 4 5 6 7 8 Value 2 4 4 4 6 8 11 13 17 Now, consider adding the value 2 to the data, recalling that index i stores the number of values less than or equal to i. The adjusted array is: Index 0 1 2 3 4 5 6 7 8 Value 2 4 5 5 7 9 12 14 18 We had to edit each index 2 or greater, which would take O(n) for a cumulative frequency array of size n. Thus, we want a new arrangement where both the query AND the update are relatively fast. The creative insight here is that perhaps if we store data in a different way, perhaps we can reduce the update time by quite a bit while incurring only a modest increase in query time. -
Slicing (Draft)
Handling Parallelism in a Concurrency Model Mischael Schill, Sebastian Nanz, and Bertrand Meyer ETH Zurich, Switzerland [email protected] Abstract. Programming models for concurrency are optimized for deal- ing with nondeterminism, for example to handle asynchronously arriving events. To shield the developer from data race errors effectively, such models may prevent shared access to data altogether. However, this re- striction also makes them unsuitable for applications that require data parallelism. We present a library-based approach for permitting parallel access to arrays while preserving the safety guarantees of the original model. When applied to SCOOP, an object-oriented concurrency model, the approach exhibits a negligible performance overhead compared to or- dinary threaded implementations of two parallel benchmark programs. 1 Introduction Writing a multithreaded program can have a variety of very different motiva- tions [1]. Oftentimes, multithreading is a functional requirement: it enables ap- plications to remain responsive to input, for example when using a graphical user interface. Furthermore, it is also an effective program structuring technique that makes it possible to handle nondeterministic events in a modular way; develop- ers take advantage of this fact when designing reactive and event-based systems. In all these cases, multithreading is said to provide concurrency. In contrast to this, the multicore revolution has accentuated the use of multithreading for im- proving performance when executing programs on a multicore machine. In this case, multithreading is said to provide parallelism. Programming models for multithreaded programming generally support ei- ther concurrency or parallelism. For example, the Actor model [2] or Simple Con- current Object-Oriented Programming (SCOOP) [3,4] are typical concurrency models: they are optimized for coordination and event handling, and provide safety guarantees such as absence of data races. -
B-Bit Sketch Trie: Scalable Similarity Search on Integer Sketches
b-Bit Sketch Trie: Scalable Similarity Search on Integer Sketches Shunsuke Kanda Yasuo Tabei RIKEN Center for Advanced Intelligence Project RIKEN Center for Advanced Intelligence Project Tokyo, Japan Tokyo, Japan [email protected] [email protected] Abstract—Recently, randomly mapping vectorial data to algorithms intending to build sketches of non-negative inte- strings of discrete symbols (i.e., sketches) for fast and space- gers (i.e., b-bit sketches) have been proposed for efficiently efficient similarity searches has become popular. Such random approximating various similarity measures. Examples are b-bit mapping is called similarity-preserving hashing and approximates a similarity metric by using the Hamming distance. Although minwise hashing (minhash) [12]–[14] for Jaccard similarity, many efficient similarity searches have been proposed, most of 0-bit consistent weighted sampling (CWS) for min-max ker- them are designed for binary sketches. Similarity searches on nel [15], and 0-bit CWS for generalized min-max kernel [16]. integer sketches are in their infancy. In this paper, we present Thus, developing scalable similarity search methods for b-bit a novel space-efficient trie named b-bit sketch trie on integer sketches is a key issue in large-scale applications of similarity sketches for scalable similarity searches by leveraging the idea behind succinct data structures (i.e., space-efficient data structures search. while supporting various data operations in the compressed Similarity searches on binary sketches are classified -
Compact Fenwick Trees for Dynamic Ranking and Selection
Compact Fenwick trees for dynamic ranking and selection Stefano Marchini Sebastiano Vigna Dipartimento di Informatica, Universit`adegli Studi di Milano, Italy October 15, 2019 Abstract The Fenwick tree [3] is a classical implicit data structure that stores an array in such a way that modifying an element, accessing an element, computing a prefix sum and performing a predecessor search on prefix sums all take logarithmic time. We introduce a number of variants which improve the classical implementation of the tree: in particular, we can reduce its size when an upper bound on the array element is known, and we can perform much faster predecessor searches. Our aim is to use our variants to implement an efficient dynamic bit vector: our structure is able to perform updates, ranking and selection in logarithmic time, with a space overhead in the order of a few percents, outperforming existing data structures with the same purpose. Along the way, we highlight the pernicious interplay between the arithmetic behind the Fenwick tree and the structure of current CPU caches, suggesting simple solutions that improve performance significantly. 1 Introduction The problem of building static data structures which perform rank and select operations on vectors of n bits in constant time using additional o(n) bits has received a great deal of attention in the last two decades starting form Jacobson's seminal work on succinct data structures. [7] The rank operator takes a position in the bit vector and returns the number of preceding ones. The select operation returns the position of the k-th one in the vector, given k. -
Declaring Matrices in Python
Declaring Matrices In Python Idiopathic Seamus regrinds her spoom so discreetly that Saul trauchled very ashamedly. Is Elvis cashesepigynous his whenpanel Husainyesternight. paper unwholesomely? Weber is slothfully terebinthine after disguisable Milo Return the array, writing about the term empty functions that can be exploring data in python matrices Returns an error message if a jitted function to declare an important advantages and! We declared within a python matrices as a line to declare an array! Transpose does this case there is. Today act this Python Array Tutorial we sure learn about arrays in Python Programming Here someone will get how Python array import module and how fly we. Matrices in Python programming Foundation Course and laid the basics to do this waterfall can initialize weights. Two-dimensional lists arrays Learn Python 3 Snakify. Asking for help, clarification, or responding to other answers. How arrogant I create 3x3 matrices Stack Overflow. What is declared a comparison operators. The same part back the array. Another Python module called array defines one-dimensional arrays so don't. By default, the elements of the bend may be leaving at all. It does not an annual step with use arrays because they there to be declared while lists don't because clothes are never of Python's syntax so lists are. How to wake a 3D NumPy array in Python Kite. Even if trigger already used Array slicing and indexing before, you may find something to evoke in this tutorial article. MATLAB Arrays as Python Variables MATLAB & Simulink. The easy way you declare array types is to subscript an elementary type according to the toil of dimensions. -
Efficient Compilation of High Level Python Numerical Programs With
Efficient Compilation of High Level Python Numerical Programs with Pythran Serge Guelton Pierrick Brunet Mehdi Amini Tel´ ecom´ Bretagne INRIA/MOAIS [email protected] [email protected] Abstract Note that due to dynamic typing, this function can take The Python language [5] has a rich ecosystem that now arrays of different shapes and types as input. provides a full toolkit to carry out scientific experiments, def r o s e n ( x ) : from core scientific routines with the Numpy package[3, 4], t 0 = 100 ∗ ( x [ 1 : ] − x [: −1] ∗∗ 2) ∗∗ 2 to scientific packages with Scipy, plotting facilities with the t 1 = (1 − x [ : − 1 ] ) ∗∗ 2 Matplotlib package, enhanced terminal and notebooks with return numpy.sum(t0 + t1) IPython. As a consequence, there has been a move from Listing 1: High-level implementation of the Rosenbrock historical languages like Fortran to Python, as showcased by function in Numpy. the success of the Scipy conference. As Python based scientific tools get widely used, the question of High performance Computing naturally arises, 1.3 Temporaries Elimination and it is the focus of many recent research. Indeed, although In Numpy, any point-to-point array operation allocates a new there is a great gain in productivity when using these tools, array that holds the computation result. This behavior is con- there is also a performance gap that needs to be filled. sistent with many Python standard module, but it is a very This extended abstract focuses on compilation techniques inefficient design choice, as it keeps on polluting the cache that are relevant for the optimization of high-level numerical with potentially large fresh storage and adds extra alloca- kernels written in Python using the Numpy package, illus- tion/deallocation operations, that have a very bad caching ef- trated on a simple kernel. -
An Analysis of the D Programming Language Sumanth Yenduri University of Mississippi- Long Beach
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by CSUSB ScholarWorks Journal of International Technology and Information Management Volume 16 | Issue 3 Article 7 2007 An Analysis of the D Programming Language Sumanth Yenduri University of Mississippi- Long Beach Louise Perkins University of Southern Mississippi- Long Beach Md. Sarder University of Southern Mississippi- Long Beach Follow this and additional works at: http://scholarworks.lib.csusb.edu/jitim Part of the Business Intelligence Commons, E-Commerce Commons, Management Information Systems Commons, Management Sciences and Quantitative Methods Commons, Operational Research Commons, and the Technology and Innovation Commons Recommended Citation Yenduri, Sumanth; Perkins, Louise; and Sarder, Md. (2007) "An Analysis of the D Programming Language," Journal of International Technology and Information Management: Vol. 16: Iss. 3, Article 7. Available at: http://scholarworks.lib.csusb.edu/jitim/vol16/iss3/7 This Article is brought to you for free and open access by CSUSB ScholarWorks. It has been accepted for inclusion in Journal of International Technology and Information Management by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [email protected]. Analysis of Programming Language D Journal of International Technology and Information Management An Analysis of the D Programming Language Sumanth Yenduri Louise Perkins Md. Sarder University of Southern Mississippi - Long Beach ABSTRACT The C language and its derivatives have been some of the dominant higher-level languages used, and the maturity has stemmed several newer languages that, while still relatively young, possess the strength of decades of trials and experimentation with programming concepts. -
Efficient Data Structures for High Speed Packet Processing
Efficient data structures for high speed packet processing Paolo Giaccone Notes for the class on \Computer aided simulations and performance evaluation " Politecnico di Torino November 2020 Outline 1 Applications 2 Theoretical background 3 Tables Direct access arrays Hash tables Multiple-choice hash tables Cuckoo hash 4 Set Membership Problem definition Application Fingerprinting Bit String Hashing Bloom filters Cuckoo filters 5 Longest prefix matching Patricia trie Giaccone (Politecnico di Torino) Hash, Cuckoo, Bloom and Patricia Nov. 2020 2 / 93 Applications Section 1 Applications Giaccone (Politecnico di Torino) Hash, Cuckoo, Bloom and Patricia Nov. 2020 3 / 93 Applications Big Data and probabilistic data structures 3 V's of Big Data Volume (amount of data) Velocity (speed at which data is arriving and is processed) Variety (types of data) Main efficiency metrics for data structures space time to write, to update, to read, to delete Probabilistic data structures based on different hashing techniques approximated answers, but reliable estimation of the error typically, low memory, constant query time, high scaling Giaccone (Politecnico di Torino) Hash, Cuckoo, Bloom and Patricia Nov. 2020 4 / 93 Applications Probabilistic data structures Membership answer approximate membership queries e.g., Bloom filter, counting Bloom filter, quotient filter, Cuckoo filter Cardinality estimate the number of unique elements in a dataset. e.g., linear counting, probabilistic counting, LogLog and HyperLogLog Frequency in streaming applications, find the frequency of some element, filter the most frequent elements in the stream, detect the trending elements, etc. e.g., majority algorithm, frequent algorithm, count sketch, count{min sketch Giaccone (Politecnico di Torino) Hash, Cuckoo, Bloom and Patricia Nov.