The role of concurrency in an evolutionary view of programming abstractions

In this paper we examine how concurrency has been embodied in mainstream programming languages. In particular, we rely on the evolutionary talking borrowed from biology to discuss major historical landmarks and crucial concepts that shaped the development of programming languages. We examine the general development process, occasionally deepening into some language, trying to uncover evolutionary lineages related to specific programming traits. We mainly focus on concurrency, discussing the different abstraction levels involved in present-day concurrent programming and emphasizing the fact that they correspond to different levels of explanation. We then comment on the role of theoretical research on the quest for suitable programming abstractions, recalling the importance of changing the working framework and the way of looking every so often. This paper is not meant to be a survey of modern mainstream programming languages: it would be very incomplete in that sense. It aims instead at pointing out a number of remarks and connect them under an evolutionary perspective, in order to grasp a unifying, but not simplistic, view of the programming languages development process

A Brief Study of Open Source Graph Databases

With the proliferation of large irregular sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis techniques to enable users to ingest, update, query and compute on the topological structure of these relationships represented as set(s) of edges between set(s) of vertices. To store and process Facebook-scale datasets, they must be able to support data sources with billions of edges, update rates of millions of updates per second, and complex analysis kernels. These platforms must provide intuitive interfaces that enable graph experts and novice programmers to write implementations of common graph algorithms. In this paper, we explore a variety of graph analysis and storage platforms. We compare their capabil- ities, interfaces, and performance by implementing and computing a set of real-world graph algorithms on synthetic graphs with up to 256 million edges. In the spirit of full disclosure, several authors are affiliated with the development of STINGER.Comment: WSSSPE13, 4 Pages, 18 Pages with Appendix, 25 figure

PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation

High-performance computing has recently seen a surge of interest in heterogeneous systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices offer tremendous potential for performance and efficiency in important large-scale applications of computational science. However, exploiting this potential can be challenging, as one must adapt to the specialized and rapidly evolving computing environment currently exhibited by GPUs. One way of addressing this challenge is to embrace better techniques and develop tools tailored to their needs. This article presents one simple technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL, two open-source toolkits that support this technique. In introducing PyCUDA and PyOpenCL, this article proposes the combination of a dynamic, high-level scripting language with the massive performance of a GPU as a compelling two-tiered computing platform, potentially offering significant performance and productivity advantages over conventional single-tier, static systems. The concept of RTCG is simple and easily implemented using existing, robust infrastructure. Nonetheless it is powerful enough to support (and encourage) the creation of custom application-specific tools by its users. The premise of the paper is illustrated by a wide range of examples where the technique has been applied with considerable success.Comment: Submitted to Parallel Computing, Elsevie

Testing embedded software in a simulated environment

Abstract. In this master’s thesis, a simulation environment that can be used to execute embedded software’s unit tests is implemented. The purpose of the simulation is to make the development of the embedded firmware easier, cheaper, and faster. Also, the purpose is to make remote work easier by enabling unit test and integration test execution on a laptop. This topic has been researched a lot before and many different solutions and tools exist for embedded system simulation. Some of these solutions are introduced in this paper. After the introduction, two of the solutions are implemented for one embedded system that uses monolithic firmware. The solutions implemented are emulation based on the Unicorn emulator and a simulation with native execution on a PC. Each solution has advantages and disadvantages. But in this case, the native execution on a PC was better, as the test execution was two times faster than in Unicorn emulator and three times faster than in an embedded device. Native execution was also easier to implement than Unicorn emulator and could use free compilers like GCC and Clang. The biggest disadvantage with native execution was the low fidelity.Sulautetun ohjelmiston testaaminen simuloidussa ympäristössä. Tiivistelmä. Tässä diplomityössä tehdään simulointiympäristö, jolla voidaan ajaa sulautetun järjestelmän yksikkö- ja integraatiotestejä. Simulaation tarkoitus on tehdä sulautetun järjestelmän ohjelmistokehitys helpommaksi, halvemmaksi ja nopeammaksi. Lisäksi simulaatiolla saadaan tehtyä etätyöskentely helpommaksi, kun yksikkö- ja integraatiotestit voidaan ajaa kannettavalla tietokoneella. Sulautetun järjestelmän simulointia on tutkittu paljon ja simulointiin on kehitetty monia eri ratkaisuja ja työkaluja. Osa näistä työkaluista esitellään tässä diplomityössä. Esittelyn jälkeen toteutetaan kaksi eri simulointi ympäristöä yhdelle sulautetulle järjestelmälle. Toteutetut simulaatiot ovat: emulaatio joka tehdään Unicorn emulaattorilla ja simulaatio joka toteutetaan natiiviajona PC:llä. Molemmilla ratkaisuilla on hyvät ja huonot puolet. Mutta kokonaisuutena natiiviajo oli parempi tälle sulautetulle järjestelmälle, koska natiiviajo oli kaksi kertaa nopeampi kuin Unicorn emulaattori ja kolme kertaa nopeampi kuin sulautettu järjestelmä. Lisäksi natiiviajo oli helpompi toteuttaa kuin Unicorn emulaattori ja natiiviajossa voitiin käytettään ilmaisia kääntäjiä kuten GCC ja Clang. Huonoin puoli natiiviajossa oli se, että natiiviajon tarkkuus ei ollut kovin hyvä, eikä sillä näin ollen pystynyt testaamaan kaikkia asioita koodista

HeAT – a Distributed and GPU-accelerated Tensor Framework for Data Analytics

In order to cope with the exponential growth in available data, the efficiency of data analysis and machine learning libraries have recently received increased attention. Although corresponding array-based numerical kernels have been significantly improved, most are limited by the resources available on a single computational node. Consequently, kernels must exploit distributed resources, e.g., distributed memory architectures. To this end, we introduce HeAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload via MPI on arbitrarily large high-performance computing systems. It provides both low-level array-based computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take advantage of their available resources, significantly lowering the barrier to distributed data analysis. Compared with applications written in similar frameworks, HeAT achieves speedups of up to two orders of magnitude