Session-Based Programming for Parallel Algorithms: Expressiveness and Performance

This paper investigates session programming and typing of benchmark examples to compare productivity, safety and performance with other communications programming languages. Parallel algorithms are used to examine the above aspects due to their extensive use of message passing for interaction, and their increasing prominence in algorithmic research with the rising availability of hardware resources such as multicore machines and clusters. We contribute new benchmark results for SJ, an extension of Java for type-safe, binary session programming, against MPJ Express, a Java messaging system based on the MPI standard. In conclusion, we observe that (1) despite rich libraries and functionality, MPI remains a low-level API, and can suffer from commonly perceived disadvantages of explicit message passing such as deadlocks and unexpected message types, and (2) the benefits of high-level session abstraction, which has significant impact on program structure to improve readability and reliability, and session type-safety can greatly facilitate the task of communications programming whilst retaining competitive performance

Using Low Level Linear Memory Management for Type-Preserving Mark-Sweep Garbage Collector

Efficient low-level systems such as garbage collectors need more control over memory than safe high-level languages usually provide. Due to this constraint, garbage collectors are typically written in unsafe languages such as C. A collector of this form usually resides as a trusted primitive runtime service outside the model of the programming language. The type safety of these languages depends on the assumption that the garbage collector will not violate any typing invariants. However, no realistic systems provide proof of this assumption. A garbage collector written in a strongly typed language can guarantee not only the safety of the garbage collector and the program being garbage collected (mutator), but also the interaction between the collector and the mutator. Removing the garbage collector from the trusted computing base has many additional benefits: Untrusted code could be given more control over memory management without sacrificing security. Low-level code such as device drivers could interface in a safe way with a garbage collector. For these and the growing prevalence of garbage collectors in the typical programming system necessitate a safe solution. Previous research by Wang et al introduced a safe copying collector based on regions, where the live graph structure of the heap is copied from an old region to a newer region. This paper seeks to improve the efficiency of type-preserving garbage collection with the introduction of a type-preserving mark and sweep garbage collector

Type-Safe Data Plane Programming

Since the mid-1990s, there have been efforts to enable more flexible processing of network packets by making packet processing programmable. With the advent of software-defined networking (SDN), this idea has now become a reality. Early approaches initially focused on control plane programming, with the goal of implementing centralized network policies at a high level of abstraction without having to use low-level, device-specific configuration mechanisms. For this purpose, various network programming languages have been developed, which provide correctness guarantees and make the formal verification of network policies possible. More recently, it is also possible to program the network data plane. Being able to define the structure of network packet headers freely, opens up a whole new range of applications, from implementing new network protocols up to moving application logic directly into the network. Until today, the P4 language has become the de facto standard for programming data planes. While P4 provides declarative abstractions for programming data planes, P4 lacks basic safety guarantees to help avoid errors and implement correct applications for the data plane. Modern programming languages use static type systems to provide languages with basic safety guarantees that completely eliminate the occurrence of entire categories of errors. Surprisingly, however, the use of type systems in the field of network programming has hardly been investigated. This dissertation investigates what appropriate type systems must look like in order to provide data plane programming languages—in particular, P4—with static correctness guarantees. As a first step, we present SafeP4, a domain-specific language for programmable data planes that is equipped with a static type system that guarantees that all headers that are read or written are valid, which is a common cause of errors. We then present Π4, whose type system is based on dependent types and is thus able to bridge the gap in terms of expressiveness between SafeP4 and full-fledged verification tools. At the same time, Π4 enables modular verification of programs. Our evaluation using open source programs confirms that accessing invalid packet headers is a common source of errors in practice and that the SafeP4’s type system is capable of identifying buggy programs. Using case studies, we show that Π4’s type system is capable of expressing and verifying a variety of real-world correctness properties

A study of systems implementation languages for the POCCNET system

The results are presented of a study of systems implementation languages for the Payload Operations Control Center Network (POCCNET). Criteria are developed for evaluating the languages, and fifteen existing languages are evaluated on the basis of these criteria

Building Efficient Query Engines in a High-Level Language

Abstraction without regret refers to the vision of using high-level programming languages for systems development without experiencing a negative impact on performance. A database system designed according to this vision offers both increased productivity and high performance, instead of sacrificing the former for the latter as is the case with existing, monolithic implementations that are hard to maintain and extend. In this article, we realize this vision in the domain of analytical query processing. We present LegoBase, a query engine written in the high-level language Scala. The key technique to regain efficiency is to apply generative programming: LegoBase performs source-to-source compilation and optimizes the entire query engine by converting the high-level Scala code to specialized, low-level C code. We show how generative programming allows to easily implement a wide spectrum of optimizations, such as introducing data partitioning or switching from a row to a column data layout, which are difficult to achieve with existing low-level query compilers that handle only queries. We demonstrate that sufficiently powerful abstractions are essential for dealing with the complexity of the optimization effort, shielding developers from compiler internals and decoupling individual optimizations from each other. We evaluate our approach with the TPC-H benchmark and show that: (a) With all optimizations enabled, LegoBase significantly outperforms a commercial database and an existing query compiler. (b) Programmers need to provide just a few hundred lines of high-level code for implementing the optimizations, instead of complicated low-level code that is required by existing query compilation approaches. (c) The compilation overhead is low compared to the overall execution time, thus making our approach usable in practice for compiling query engines

An Introduction to Programming for Bioscientists: A Python-based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables, numerous exercises, and 19 pages of Supporting Information; currently in press at PLOS Computational Biolog

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

Dynamically typed languages

Dynamically typed languages such as Python and Ruby have experienced a rapid grown in popularity in recent times. However, there is much confusion as to what makes these languages interesting relative to statically typed languages, and little knowledge of their rich history. In this chapter I explore the general topic of dynamically typed languages, how they differ from statically typed languages, their history, and their defining features

Description and Optimization of Abstract Machines in a Dialect of Prolog

In order to achieve competitive performance, abstract machines for Prolog and related languages end up being large and intricate, and incorporate sophisticated optimizations, both at the design and at the implementation levels. At the same time, efficiency considerations make it necessary to use low-level languages in their implementation. This makes them laborious to code, optimize, and, especially, maintain and extend. Writing the abstract machine (and ancillary code) in a higher-level language can help tame this inherent complexity. We show how the semantics of most basic components of an efficient virtual machine for Prolog can be described using (a variant of) Prolog. These descriptions are then compiled to C and assembled to build a complete bytecode emulator. Thanks to the high level of the language used and its closeness to Prolog, the abstract machine description can be manipulated using standard Prolog compilation and optimization techniques with relative ease. We also show how, by applying program transformations selectively, we obtain abstract machine implementations whose performance can match and even exceed that of state-of-the-art, highly-tuned, hand-crafted emulators.Comment: 56 pages, 46 figures, 5 tables, To appear in Theory and Practice of Logic Programming (TPLP