QuantumInformation.jl---a Julia package for numerical computation in quantum information theory

Numerical investigations are an important research tool in quantum information theory. There already exists a wide range of computational tools for quantum information theory implemented in various programming languages. However, there is little effort in implementing this kind of tools in the Julia language. Julia is a modern programming language designed for numerical computation with excellent support for vector and matrix algebra, extended type system that allows for implementation of elegant application interfaces and support for parallel and distributed computing. QuantumInformation.jl is a new quantum information theory library implemented in Julia that provides functions for creating and analyzing quantum states, and for creating quantum operations in various representations. An additional feature of the library is a collection of functions for sampling random quantum states and operations such as unitary operations and generic quantum channels.Comment: 32 pages, 8 figure

A Simple Vector Language and its Portable Implementation

Many explicitly parallel languages have been proposed and implemented, but most such languages are complex and are targeted to specific parallel machines. The goal of this project was to design a very simple, explicitly parallel, programming language which could easily be implemented and ported to a wide variety of machines. The result was AJL, a structured language with deterministic vector-oriented parallelism. AJL programs are first compiled into assembly language instructions for an idealized parallel machine, then these assembly language instructions are macro expanded into C code which implements them for the actual target machine. Finally, the target machine’s “ native” C compiler is used to generate executable code. Macro definitions for “ generic” sequential machines have been implemented; macros for the PASM (PArtitionable Simd Mimd) prototype parallel computer are under development

Intermediate Code Generation for Portable Scalable, Compilers. Architecture Independent Data Parallelism: The Preliminaries

This paper introduces the goals of the Portable, Scalable, Architecture Independent (PSI) Compiler Project for Data Parallel Languages at the University of Missouri-Rolla. A goal of this project is to produce a subcompiler for data parallel scientific programming languages such as HPF(High Performance Fortran) where the input grammar is translated to a three-address code intermediate language. Ultimately we plan to integrate our work into automated synthesis systems for scientific programming because we feel that it should not be necessary to learn complicated programming techniques to use multiprocessor computers or networks of computers effectively. This paper shows how to compile a data parallel language to an arbitrary multiprocessor topology or network of CPUs given the number of processors, length of vector registers, and total number of components in an array assuming a message passing, distributed memory paradigm of send and receive. We emphasize that this paradigm is not only amenable to machines such as the CM5 and NCube but to LAN and WAN connected architectures. We do automatic program partitioning and mapping to processing elements of a multiprocessor architecture or distributed network of machines. No programmer intervention is required, hence, no errors will be introduced through data decomposition

SAR: Learning Cross-Language API Mappings with Little Knowledge

To save effort, developers often translate programs from one programming language to another, instead of implementing it from scratch. Translating application program interfaces (APIs) used in one language to functionally equivalent ones available in another language is an important aspect of program translation. Existing approaches facilitate the translation by automatically identifying the API mappings across programming languages. However, these approaches still require a large number of parallel corpora, ranging from pairs of APIs or code fragments that are functionally equivalent, to similar code comments. To minimize the need for parallel corpora, this paper aims at an automated approach that can map APIs across languages with much less a priori knowledge than other approaches. Our approach is based on a realization of the notion of domain adaption, combined with code embedding, to better align two vector spaces. Taking as input large sets of programs, our approach first generates numeric vector representations of the programs (including the APIs used in each language), and it adapts generative adversarial networks (GAN) to align the vectors in different spaces of two languages. For better alignment, we initialize the GAN with parameters derived from API mapping seeds that can be identified accurately with a simple automatic signature-based matching heuristic. Then the cross-language API mappings can be identified via nearest-neighbors queries in the aligned vector spaces. We have implemented the approach (SAR, named after three main technical components, Seeding, Adversarial training, and Refinement) in a prototype for mapping APIs across Java and C# programs. Our evaluation on about 2 million Java files and 1 million C# files shows that the approach can achieve 48% and 78% mapping accuracy in its top-1 and top-10 API mapping results respectively, with only 174 automatically identified seeds, which is more accurate than other approaches using the same or much more mapping seeds

Parallel Programming Recipes

Parallel programming has become vital for the success of commercial applications since Moore’s Law will now be used to double the processors (or cores) per chip every technology generation. The performance of applications depends on how software executions can be mapped on the multi-core chip, and how efficiently they run the cores. Currently, the increase of parallelism in software development is necessary, not only for taking advantage of multi-core capability, but also for adapting and surviving in the new silicon implementation. This project will provide the performance characteristics of parallelism for some common algorithms or computations using different parallel languages. Based on concrete experiments, where each algorithm is implemented on different languages and the program’s performance is measured, the project provides the recipes for the problem computations. The following are the central problems and algorithms of the project: Arithmetic Algebra: Maclaurin Series Calculation for ex, Dot-Product of Two Vectors: each vector has size n; Sort Algorithms: Bubble sort, Odd-Event sort; Graphics: Graphics rendering. The languages are chosen based on commonality in the current market and ease of use; i.e., OpenMP, MPI, and OpenCL. The purpose of this study is to provide reader a broad knowledge about parallel programming, the comparisons, in terms of performance and implementation cost, across languages and application types. It is hoped to be very useful for programmers/computer-architects to decide which language to use for a certain applications/problems and cost estimations for the projects. Also, it is hoped that the project can be expanded in the future so that more languages/technologies as well as applications can be analyze

Array languages and the N-body problem

This paper is a description of the contributions to the SICSA multicore challenge on many body planetary simulation made by a compiler group at the University of Glasgow. Our group is part of the Computer Vision and Graphics research group and we have for some years been developing array compilers because we think these are a good tool both for expressing graphics algorithms and for exploiting the parallelism that computer vision applications require. We shall describe experiments using two languages on two different platforms and we shall compare the performance of these with reference C implementations running on the same platforms. Finally we shall draw conclusions both about the viability of the array language approach as compared to other approaches used in the challenge and also about the strengths and weaknesses of the two, very different, processor architectures we used

Contract-Based General-Purpose GPU Programming

Using GPUs as general-purpose processors has revolutionized parallel computing by offering, for a large and growing set of algorithms, massive data-parallelization on desktop machines. An obstacle to widespread adoption, however, is the difficulty of programming them and the low-level control of the hardware required to achieve good performance. This paper suggests a programming library, SafeGPU, that aims at striking a balance between programmer productivity and performance, by making GPU data-parallel operations accessible from within a classical object-oriented programming language. The solution is integrated with the design-by-contract approach, which increases confidence in functional program correctness by embedding executable program specifications into the program text. We show that our library leads to modular and maintainable code that is accessible to GPGPU non-experts, while providing performance that is comparable with hand-written CUDA code. Furthermore, runtime contract checking turns out to be feasible, as the contracts can be executed on the GPU

C Language Extensions for Hybrid CPU/GPU Programming with StarPU

Modern platforms used for high-performance computing (HPC) include machines with both general-purpose CPUs, and "accelerators", often in the form of graphical processing units (GPUs). StarPU is a C library to exploit such platforms. It provides users with ways to define "tasks" to be executed on CPUs or GPUs, along with the dependencies among them, and by automatically scheduling them over all the available processing units. In doing so, it also relieves programmers from the need to know the underlying architecture details: it adapts to the available CPUs and GPUs, and automatically transfers data between main memory and GPUs as needed. While StarPU's approach is successful at addressing run-time scheduling issues, being a C library makes for a poor and error-prone programming interface. This paper presents an effort started in 2011 to promote some of the concepts exported by the library as C language constructs, by means of an extension of the GCC compiler suite. Our main contribution is the design and implementation of language extensions that map to StarPU's task programming paradigm. We argue that the proposed extensions make it easier to get started with StarPU,eliminate errors that can occur when using the C library, and help diagnose possible mistakes. We conclude on future work

Julia: A Fresh Approach to Numerical Computing

Bridging cultures that have often been distant, Julia combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing. Julia is designed to be easy and fast. Julia questions notions generally held as "laws of nature" by practitioners of numerical computing: 1. High-level dynamic programs have to be slow. 2. One must prototype in one language and then rewrite in another language for speed or deployment, and 3. There are parts of a system for the programmer, and other parts best left untouched as they are built by the experts. We introduce the Julia programming language and its design --- a dance between specialization and abstraction. Specialization allows for custom treatment. Multiple dispatch, a technique from computer science, picks the right algorithm for the right circumstance. Abstraction, what good computation is really about, recognizes what remains the same after differences are stripped away. Abstractions in mathematics are captured as code through another technique from computer science, generic programming. Julia shows that one can have machine performance without sacrificing human convenience.Comment: 37 page

cphVB: A System for Automated Runtime Optimization and Parallelization of Vectorized Applications

Modern processor architectures, in addition to having still more cores, also require still more consideration to memory-layout in order to run at full capacity. The usefulness of most languages is deprecating as their abstractions, structures or objects are hard to map onto modern processor architectures efficiently. The work in this paper introduces a new abstract machine framework, cphVB, that enables vector oriented high-level programming languages to map onto a broad range of architectures efficiently. The idea is to close the gap between high-level languages and hardware optimized low-level implementations. By translating high-level vector operations into an intermediate vector bytecode, cphVB enables specialized vector engines to efficiently execute the vector operations. The primary success parameters are to maintain a complete abstraction from low-level details and to provide efficient code execution across different, modern, processors. We evaluate the presented design through a setup that targets multi-core CPU architectures. We evaluate the performance of the implementation using Python implementations of well-known algorithms: a jacobi solver, a kNN search, a shallow water simulation and a synthetic stencil simulation. All demonstrate good performance