A high performance Erlang Tcp/Ip stack

Functional languages are not often associated with the development of network stacks, mainly due to the lower performance and lack of support for system programming than more conventional languages such as C. However, there are functional languages that offer features which make it easier to develop network protocols than using a more conventional approach based on an imperative language. Erlang, for instance, offers support for distribution, concurrency and soft real time built-in into the language. All those features are desirable to ease the development of network protocol implementations. It is also possible to implement a reasonably efficient network stack in this language, provided some precautions are taken in the design. By using Erlang distribution it is possible to support fault tolerant distributed TCP connections that take advantage of the distributed nature of the applications implemented in Erlang to provide low cost synchronization, with just some support from the application itself.

Current (heterogeneous) multi-core environments such as gpGPU architectures are hard to program with normal imperative and object-oriented (OO) languages. There are two basic problems to tackle: (1) it is too easy to program race conditions and dead-locks with the available synchronization primitives, and (2) these environments do not support (or support inefficiently) the instructions required for efficient execution of OO programs, e.g., because function pointers and pointer arithmetic are lacking. We address both problems with a new language that comprises both Functional Programming (FP) and OO programming. We solve problem (1) by auto-parallelization in the functional core where all loops and non-dependent calls can be executed in parallel. FP is to be used to write computational intensive code with safe concurrent memory access. An alternative object model that does neither use pointer arithmetic nor function pointers but smart pointers/proxies (to implement polymorphism) as well as mixins and templates (to implement OO like code reuse) solves problem (2). To cleanly integrate the two language cores, we propose a new integration model that even grants some restricted ways to access state from within FP mode. With the new language and prototype compiler we can transparently parallelize code to target both Cuda and multi-core machines (without annotations from the programmer) and obtain good speedups.

Proceedings of the 13th ACM SIGPLAN international conference on Functional programming

Networks for the Internet of Things typically use a gateway to provide connectivity between a low bit rate, low capability sensor network and the broader Internet. The gateway can be subject to very high traffic loads, many concurrent processes and needs to be highly reliable. Functional programming languages such as Erlang and Elixir have proven to be an effective programming paradigm for such scenarios, notably in large-scale telecommunications switches. In this paper, we report on our experience of developing a gateway between a LoRa network and an MQTT broker using the functional programming language Elixir and the more conventional language C++. To obtain an understanding of this approach to development, we first developed an initial prototype on a single-board computer using Elixir. We then developed the same system in C++ and ran experiments to compare the two systems’ performance. In order to understand the performance of such systems on low-end IoT devices, we then developed the same system on a low-cost ESP32 micro-controller in both C++ and Elixir. We were able to run the Elixir-based system on an ESP32 micro-controller but found that its performance was significantly poorer than the same system written in C++. We conclude that functional programming has great potential for the development of IoT systems, but work needs to be carried out to improve the supporting libraries and underlying virtual machines. We also note that learning to program in a functional programming language has quite a steep learning curve.

Parallel functional languages often use meta-linguistic annotations to provide control over parallel evaluation. In this paper we explore a flexible mechanism to control when an expression is evaluated: first-class monadic schedules . We discuss the advantages of using such first-class values over traditional annotation-based systems. In particular, it is often desirable to make decisions about the operational behavior of parallel programs depending on the dynamic state of the system. For example, we may want to measure the system load before deciding to evaluate expressions in parallel. For this purpose, we show how monads can be used to access dynamic system parameters in a referentially transparent manner (up to termination).As a mechanism to reason about schedules, we present a set of algebraic properties that any implementation of schedules must satisfy. We also describe an implementation that translates schedules into a dialect of Scheme extended with futures . We prove that this implementation satisfies the given set of algebraic properties, and give performance results for a parallel solution to the n -body problem using the Barnes--Hut method.Although our ideas were developed specifically for nonstrict functional languages such as Haskell, we briefly discuss how they can be used with strict functional languages and imperative languages as well.

The pedagogical desirability of functional programming is effectively swamped by an overwhelmingly imperative‐language‐oriented culture. Because a meaningful subset of the functional paradigm is accessible within imperative languages, it is possible to identify a subset of Modula‐2 that characterizes the functional paradigm subset. A series of list‐processing problems and their solutions in the Modula‐2 subset provides students with ample exposure to the practice and theory of functional programming. Extension to the full functional paradigm and migration to specialized functional languages are managed, in the worst case by initial pseudo‐Modula‐2 programming involving hypothetical extensions supporting higher‐order functions and polymorphism. More powerful solutions to this problem are foreshadowed, in the form of preprocessor implementations of Modula‐2‐ and Ada‐derivative functional languages.

Optimizing programs to run efficiently on modern parallel hardware is hard but crucial for many applications. The predominantly used imperative languages - like C or OpenCL - force the programmer to intertwine the code describing functionality and optimizations. This results in a portability nightmare that is particularly problematic given the accelerating trend towards specialized hardware devices to further increase efficiency. Many emerging DSLs used in performance demanding domains such as deep learning or high-performance image processing attempt to simplify or even fully automate the optimization process. Using a high-level - often functional - language, programmers focus on describing functionality in a declarative way. In some systems such as Halide or TVM, a separate schedule specifies how the program should be optimized. Unfortunately, these schedules are not written in well-defined programming languages. Instead, they are implemented as a set of ad-hoc predefined APIs that the compiler writers have exposed. In this functional pearl, we show how to employ functional programming techniques to solve this challenge with elegance. We present two functional languages that work together - each addressing a separate concern. RISE is a functional language for expressing computations using well known functional data-parallel patterns. ELEVATE is a functional language for describing optimization strategies. A high-level RISE program is transformed into a low-level form using optimization strategies written in ELEVATE . From the rewritten low-level program high-performance parallel code is automatically generated. In contrast to existing high-performance domain-specific systems with scheduling APIs, in our approach programmers are not restricted to a set of built-in operations and optimizations but freely define their own computational patterns in RISE and optimization strategies in ELEVATE in a composable and reusable way. We show how our holistic functional approach achieves competitive performance with the state-of-the-art imperative systems Halide and TVM.

In the era of scalable and high-performance software systems, functional programming has emerged as a powerful paradigm for improving code quality, modularity, and runtime efficiency. This study investigates the impact of advanced functional programming on Java application optimization through a comparative analysis of three JVM languages: Java, Scala, and Kotlin. By implementing functional constructs such as higher-order functions, immutability, pure functions, and lazy evaluation across three core application tasks—data processing, reactive systems, and algorithmic computation—this research evaluates execution time, memory usage, code complexity, and maintainability. The findings indicate that Scala consistently outperforms Java and Kotlin in execution speed, memory efficiency, and maintainability index, thanks to its strong native support for functional programming. Kotlin demonstrates a balanced performance, offering concise syntax and functional flexibility while maintaining Java interoperability. Java, despite incorporating functional features since Java 8, lags behind in terms of performance and code conciseness due to its object-oriented foundation. The study concludes that adopting Scala or Kotlin for functional programming can significantly enhance the performance and sustainability of modern JVM applications. These insights serve as a practical guide for developers and organizations aiming to modernize legacy systems or adopt functional practices in enterprise software development.

The 1990s saw a hugely productive interaction between database and programming language research. Ideas about type systems from programming languages played a central role in generalizing and adapting relational database systems to new data models. At the same time databases provided some of the best concrete examples of the application of concurrency theory and of the benefits of high-level optimization in functional programming languages. One of the driving ambitions behind this research was the idea that database access should be properly embedded in programming languages: one should not have to be bilingual in order to use a database from a programming language; and that goal has to some extent been realized. In the past fifteen years, new data models, both for data storage and for data exchange have appeared with depressing regularity and with each such model, the inevitable query language. Does programming language research have anything to contribute to these new languages? Should we take the time to to worry about embedding these models in conventional languages? Over the same period, some interesting new connections between databases and programming languages have emerged, notably in the areas of scientific databases, annotation and provenance. Will this provide new opportunities for cross-fertilization?

In this chapter, we present the main properties of functional programming. In functional programming, computation proceeds by rewriting functions and not by modifying the state. The fundamental characteristic of the languages in this paradigm, at least in their “pure” form, is precisely that of not possessing the concept of memory (and therefore side effect). Once an environment is fixed, an expression always denotes the same value. We will discuss the pure paradigm in the first sections, explaining the fundamental aspects. Functional programming languages, however, merge these “pure” ingredients in a context that adds many other mechanisms; we will review them before touching upon the SECD machine, an abstract machine for higher-order functional languages which constitutes the prototype of many real implementations. We will, at this point, be in a position to discuss the reasons why the functional programming paradigm is interesting with respect to ordinary imperative languages. The chapter concludes with a more theoretical section which provides a succinct introduction to the lambda-calculus, a formal system for computability which inspires all functional languages and which has been a constant model for the design of programming languages.KeywordsNormal FormRecursive FunctionFunctional ProgrammingAbstract MachineFunctional LanguageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We explore functional programming through a 16-week internship at Los Alamos National Laboratory. Functional programming is a branch of computer science that has exploded in popularity over the past decade due to its high-level syntax, ease of parallelization, and abundant applications. First, we summarize functional programming by listing the advantages of functional programming languages over the usual imperative languages, and we introduce the concept of parsing. Second, we discuss the importance of lambda calculus in the theory of functional programming. Lambda calculus was invented by Alonzo Church in the 1930s to formalize the concept of effective computability, and every functional language is essentially some implementation of lambda calculus. Finally, we display the lasting products of the internship: additions to a compiler and runtime system for the pure functional language STG, including both a set of tests that indicate the validity of updates to the compiler and a compiler pass that checks for illegal instances of duplicate names.

In recent years the computing landscape has seen an increasing shift towards specialized accelerators. Field programmable gate arrays (FPGAs) are particularly promising for the implementation of these accelerators, as they offer significant performance and energy improvements over CPUs for a wide class of applications and are far more flexible than fixed-function ASICs. However, FPGAs are difficult to program. Traditional programming models for reconfigurable logic use low-level hardware description languages like Verilog and VHDL, which have none of the productivity features of modern software languages but produce very efficient designs, and low-level software languages like C and OpenCL coupled with high-level synthesis (HLS) tools that typically produce designs that are far less efficient. Functional languages with parallel patterns are a better fit for hardware generation because they provide high-level abstractions to programmers with little experience in hardware design and avoid many of the problems faced when generating hardware from imperative languages. In this paper, we identify two important optimizations for using parallel patterns to generate efficient hardware: tiling and metapipelining. We present a general representation of tiled parallel patterns, and provide rules for automatically tiling patterns and generating metapipelines. We demonstrate experimentally that these optimizations result in speedups up to 39.4× on a set of benchmarks from the data analytics domain.

The ability to verify and reason about programs was one of the main goals behind the design and development of Lucid. In fact, from its very inception Lucid was intended to be not just a programming language, but also a formal system in which program specifications and correctness proofs could be expressed as well. Our goal was a formal programming system in which programs for “realistically” complicated problems could be proved correct with only a realistic amount of effort. The nonprocedural nature of Lucid and its mathematical semantics led naturally to a reasoning technique where properties proved of variables and functions (assertions) are true throughout the where clauses in which the variables and functions are defined, and are not just thought of as being true at particular points where they are “attached” (as would be the case in the usual verification technique for procedural programs). In that respect, Lucid verification is like verification of functional programs. The assertions proved for Lucid, however, have the basic simplicity of the assertions proved in the procedural case, and the proofs are very similar. We will illustrate the differences by using a very simple program for computing integer square roots. We will express it in an imperative language, in a functional language, and in Lucid and show how verification would be performed for each language using the different techniques. In all three techniques, we will use the following terminology from the procedural approach, namely that the precondition is a restriction on the inputs of a program and the postcondition is a desired property of the outputs of the program, assuming that the inputs satisfied the precondition.

Concolic testing for functional languages

In 1984, John Hughes wrote a seminal paper titled, "Why Functional Programming Matters," in which he eloquently explained the value of pure and lazy functional programming. Due to the increasing importance of the Web and the introduction of many-core machines, in the quarter of a century since the paper was written, the problems associated with imperative languages and their side effects have become increasingly evident. This talk argues that fundamentalist functional programming-that is, radically eliminating all side effects from programming languages, including strict evaluation-is what it takes to conquer the concurrency and parallelism dragon. Programmers must embrace pure, lazy functional programming "all the way"-with all effects apparent in the type system of the host language using monads. A radical paradigm shift is the answer, but does that mean that all current programmers will be lost along the way? Fortunately not! By design, LINQ is based on monadic principles, and the success of LINQ proves that the world does not fear the monads.