arXiv subjects

Phil Trinder

Publications and source records attributed to Phil Trinder.

9 recordsLinked to original sources

arXiv2025

Signalling Health for Improved Kubernetes Microservice Availability

Microservices are often deployed and managed by a container orchestrator that can detect and fix failures to maintain the service availability critical in many applications. In Poll-based Container Monitoring (PCM), the orchestrator periodically checks container health. While a common approach, PCM requires careful tuning, may degrade service availability, and can be slow to detect container health changes. An alternative is Signal-based Container Monitoring (SCM), where the container signals the orchestrator when its status changes. We present the design, implementation, and evaluation of an SCM approach for Kubernetes and empirically show that it has benefits over PCM, as predicted by a new mathematical model. We compare the service availability of SCM and PCM over six experiments using the SockShop benchmark. SCM does not require that polling intervals are tuned, and yet detects container failure 86\% faster than PCM and container readiness in a comparable time with limited resource overheads. We find PCM can erroneously detect failures, and this reduces service availability by 4\%. We propose that orchestrators offer SCM features for faster failure detection than PCM without erroneous detections or careful tuning.

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arXiv2025

Comparing Parallel Functional Array Languages: Programming and Performance

Parallel functional array languages are an emerging class of programming languages that promise to combine low-effort parallel programming with good performance and performance portability. We systematically compare the designs and implementations of five different functional array languages: Accelerate, APL, DaCe, Futhark, and SaC. We demonstrate the expressiveness of functional array programming by means of four challenging benchmarks, namely N-body simulation, MultiGrid, Quickhull, and Flash Attention. These benchmarks represent a range of application domains and parallel computational models. We argue that the functional array code is much shorter and more comprehensible than the hand-optimized baseline implementations because it omits architecture-specific aspects. Instead, the language implementations generate both multicore and GPU executables from a single source code base. Hence, we further argue that functional array code could more easily be ported to, and optimized for, new parallel architectures than conventional implementations of numerical kernels. We demonstrate this potential by reporting the performance of the five parallel functional array languages on a total of 39 instances of the four benchmarks on both a 32-core AMD EPYC 7313 multicore system and on an NVIDIA A30 GPU. We explore in-depth why each language performs well or not so well on each benchmark and architecture. We argue that the results demonstrate that mature functional array languages have the potential to deliver performance competitive with the best available conventional techniques.

cs.PL↗

arXiv2023

Special Delivery: Programming with Mailbox Types (Extended Version)

The asynchronous and unidirectional communication model supported by mailboxes is a key reason for the success of actor languages like Erlang and Elixir for implementing reliable and scalable distributed systems. Although actors eliminate many of the issues stemming from shared memory concurrency, they remain vulnerable to communication errors such as protocol violations and deadlocks. Behavioural types make it possible to detect communication errors early in the development process, but most work has addressed channel-based languages rather than actor languages. Mailbox types are a novel behavioural type system for actors first introduced for a process calculus, and capture the contents of a mailbox as a commutative regular expression. Due to aliasing and nested evaluation contexts, moving from a process calculus to a programming language is challenging. This paper presents Pat, the first programming language incorporating mailbox types, and describes an algorithmic type system. Pat is a higher-order functional language with sums, products, and lists, along with interfaces that allow finer-grained reasoning about mailbox contents. Typechecking in Pat detects four classes of behavioural error: protocol violation, unexpected message, forgotten reply and self-deadlock, as well as the usual data type errors. The Pat type system makes essential use of quasi-linear typing to tame some of the complexity introduced by aliasing. We make use of a co-contextual algorithmic type system, achieved through a novel use of backwards bidirectional typing, and we prove it sound and complete with respect to our declarative type system. We implement a type checker, and use it to demonstrate the expressiveness of Pat on a factory automation case study and a series of examples from the Savina actor benchmark suite. This establishes a foundation for applying mailbox typing to practical actor languages.

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arXiv2022

Do Fewer Tiers Mean Fewer Tears? Eliminating Web Stack Components to Improve Interoperability

Web applications are structured as multi-tier stacks of components. Each component may be written in a different language and interoperate using a variety of protocols. Such interoperation increases developer effort, can introduce security vulnerabilities, may reduce performance and require additional resources. A range of approaches have been explored to minimise web stack interoperation. This paper explores a pragmatic approach to reducing web stack interoperation, namely eliminating a tier/component. That is, we explore the implications of eliminating the Apache web server in a JAPyL web stack: Jupyter Notebook, Apache, Python, Linux, and replacing it with PHP libraries. We conduct a systematic study to investigate the implications for web stack performance, resource consumption, security, and programming effort.

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arXiv2022

Parallel Flowshop in YewPar

Parallelism may reduce the time to find exact solutions for many Operations Research (OR) problems, but parallelising combinatorial search is extremely challenging. YewPar is a new combinatorial search framework designed to allow domain specialists to benefit from parallelism by reusing sophisticated parallel search patterns. This paper shows (1) that it is low effort to encode and parallelise a typical OR problem (Flowshop Scheduling FSP) in YewPar even for scalable clusters; (2) that the YewPar library makes it extremely easy to exploit three alternate FSP parallelisations; (3) that the YewPar FSP implementations are valid, and have sequential performance comparable with a published algorithm; and (4) provides a systematic performance evaluation of the three parallel FSP versions on 10 standard FSP instances with up to 240 workers on a Beowulf cluster.

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arXiv2021

Sketch-Guided Equality Saturation: Scaling Equality Saturation to Complex Optimizations of Functional Programs

Generating high-performance code for diverse hardware and application domains is challenging. Functional array programming languages with patterns like map and reduce have been successfully combined with term rewriting to define and explore optimization spaces. However, deciding what sequence of rewrites to apply is hard and has a huge impact on the performance of the rewritten program. Equality saturation avoids the issue by exploring many possible ways to apply rewrites, efficiently representing many equivalent programs in an e-graph data structure. Equality saturation has some limitations when rewriting functional language terms, as currently naive encodings of the lambda calculus are used. We present new techniques for encoding polymorphically typed lambda calculi, and show that the efficient encoding reduces the runtime and memory consumption of equality saturation by orders of magnitude. Moreover, equality saturation does not yet scale to complex compiler optimizations. These emerge from long rewrite sequences of thousands of rewrite steps, and may use pathological combinations of rewrite rules that cause the e-graph to quickly grow too large. This paper introduces \emph{sketch-guided equality saturation}, a semi-automatic technique that allows programmers to provide program sketches to guide rewriting. Sketch-guided equality saturation is evaluated for seven complex matrix multiplication optimizations, including loop blocking, vectorization, and multi-threading. Even with efficient lambda calculus encoding, unguided equality saturation can locate only the two simplest of these optimizations, the remaining five are undiscovered even with an hour of compilation time and 60GB of RAM. By specifying three or fewer sketch guides all seven optimizations are found in seconds of compilation time, using under 1GB of RAM, and generating high performance code.

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arXiv2018

Arrows for Parallel Computation

Arrows are a general interface for computation and an alternative to Monads for API design. In contrast to Monad-based parallelism, we explore the use of Arrows for specifying generalised parallelism. Specifically, we define an Arrow-based language and implement it using multiple parallel Haskells. As each parallel computation is an Arrow, such parallel Arrows (PArrows) can be readily composed and transformed as such. To allow for more sophisticated communication schemes between computation nodes in distributed systems, we utilise the concept of Futures to wrap direct communication. To show that PArrows have similar expressive power as existing parallel languages, we implement several algorithmic skeletons and four benchmarks. Benchmarks show that our framework does not induce any notable performance overhead. We conclude that Arrows have considerable potential for composing parallel programs and for producing programs that can execute on multiple parallel language implementations.

cs.PL↗

arXiv2017

Scaling Reliably: Improving the Scalability of the Erlang Distributed Actor Platform

Distributed actor languages are an effective means of constructing scalable reliable systems, and the Erlang programming language has a well-established and influential model. While Erlang model conceptually provides reliable scalability, it has some inherent scalability limits and these force developers to depart from the model at scale. This article establishes the scalability limits of Erlang systems, and reports the work to improve the language scalability. We systematically study the scalability limits of Erlang and address the issues at the virtual machine (VM), language, and tool levels. More specifically: (1) We have evolved the Erlang VM so that it can work effectively in large scale single-host multicore and NUMA architectures. We have made important architectural improvements to the Erlang/OTP. (2) We have designed and implemented Scalable Distributed (SD) Erlang libraries to address language-level scalability issues, and provided and validated a set of semantics for the new language constructs. (3) To make large Erlang systems easier to deploy, monitor, and debug we have developed and made open source releases of five complementary tools, some specific to SD Erlang. Throughout the article we use two case studies to investigate the capabilities of our new technologies and tools: a distributed hash table based Orbit calculation and Ant Colony Optimisation (ACO). Chaos Monkey experiments show that two versions of ACO survive random process failure and hence that SD Erlang preserves the Erlang reliability model. Even for programs with no global recovery data to maintain, SD Erlang partitions the network to reduce network traffic and hence improves performance of the Orbit and ACO benchmarks above 80 hosts. ACO measurements show that maintaining global recovery data dramatically limits scalability; however scalability is recovered by partitioning the recovery data.

cs.PL↗

arXiv2017

Replicable Parallel Branch and Bound Search

Combinatorial branch and bound searches are a common technique for solving global optimisation and decision problems. Their performance often depends on good search order heuristics, refined over decades of algorithms research. Parallel search necessarily deviates from the sequential search order, sometimes dramatically and unpredictably, e.g. by distributing work at random. This can disrupt effective search order heuristics and lead to unexpected and highly variable parallel performance. The variability makes it hard to reason about the parallel performance of combinatorial searches. This paper presents a generic parallel branch and bound skeleton, implemented in Haskell, with replicable parallel performance. The skeleton aims to preserve the search order heuristic by distributing work in an ordered fashion, closely following the sequential search order. We demonstrate the generality of the approach by applying the skeleton to 40 instances of three combinatorial problems: Maximum Clique, 0/1 Knapsack and Travelling Salesperson. The overheads of our Haskell skeleton are reasonable: giving slowdown factors of between 1.9 and 6.2 compared with a class-leading, dedicated, and highly optimised C++ Maximum Clique solver. We demonstrate scaling up to 200 cores of a Beowulf cluster, achieving speedups of 100x for several Maximum Clique instances. We demonstrate low variance of parallel performance across all instances of the three combinatorial problems and at all scales up to 200 cores, with median Relative Standard Deviation (RSD) below 2%. Parallel solvers that do not follow the sequential search order exhibit far higher variance, with median RSD exceeding 85% for Knapsack.

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