Writing concurrent programs is notoriously difficult and is of increasing practical importance. A particular source of concern is that even correctly implemented concurrency abstractions cannot be composed together to form larger abstractions. In this paper we present a concurrency model, based on transactional memory, that offers far richer composition. All the usual benefits of transactional memory are present (e.g., freedom from low-level deadlock), but in addition we describe modular forms of blocking and choice that were inaccessible in earlier work. intRoDuctionThe free lunch is over. 25 We have been used to the idea that our programs will go faster when we buy a next-generation processor, but that time has passed. While that nextgeneration chip will have more CPUs, each individual CPU will be no faster than the previous year's model. If we want our programs to run faster, we must learn to write parallel programs.Writing parallel programs is notoriously tricky. Mainstream lock-based abstractions are difficult to use and they make it hard to design computer systems that are reliable and scalable. Furthermore, systems built using locks are difficult to compose without knowing about their internals.To address some of these difficulties, several researchers (including ourselves) have proposed building programming language features over software transactional memory (STM), which can perform groups of memory operations atomically. 23 Using transactional memory instead of locks brings well-known advantages: freedom from deadlock and priority inversion, automatic roll-back on exceptions or timeouts, and freedom from the tension between lock granularity and concurrency.Early work on software transactional memory suffered several shortcomings. Firstly, it did not prevent transactional code from bypassing the STM interface and accessing data directly at the same time as it is being accessed within a transaction. Such conflicts can go undetected and prevent transactions executing atomically. Furthermore, early STM systems did not provide a convincing story for building operations that may block-for example, a shared work-queue supporting operations that wait if the queue becomes empty.Our work on STM-Haskell set out to address these problems. In particular, our original paper makes the following contributions:

No abstract

We describe the design and current status of our effort to implement the programming model of nested data parallelism into the Glasgow Haskell Compiler. We extended the original programming model and its implementation, both of which were first popularised by the NESL language, in terms of expressiveness as well as efficiency. Our current aim is to provide a convenient programming environment for SMP parallelism, and especially multicore architectures. Preliminary benchmarks show that we are, at least for some programs, able to achieve good absolute performance and excellent speedups.

No abstract

Higher-order languages that encourage currying are typically implemented using one of two basic evaluation models: push/enter or eval/apply. Implementors use their intuition and qualitative judgements to choose one model or the other. Our goal in this paper is to provide, for the first time, a more substantial basis for this choice, based on our qualitative and quantitative experience of implementing both models in a state-of-the-art compiler for Haskell. Our conclusion is simple, and contradicts our initial intuition: compiled implementations should use eval/apply.

We present a new programming model for deterministic parallel computation in a pure functional language. The model is monadic and has explicit granularity, but allows dynamic construction of dataflow networks that are scheduled at runtime, while remaining deterministic and pure. The implementation is based on monadic concurrency, which has until now only been used to simulate concurrency in functional languages, rather than to provide parallelism. We present the API with its semantics, and argue that parallel execution is deterministic. Furthermore, we present a complete work-stealing scheduler implemented as a Haskell library, and we show that it performs at least as well as the existing parallel programming models in Haskell.

Abstract. Haskell provides a rich set of abstractions for parallel and concurrent programming. This tutorial covers the basic concepts involved in writing parallel and concurrent programs in Haskell, and takes a deliberately practical approach: most of the examples are real Haskell programs that you can compile, run, measure, modify and experiment with. We cover parallel programming with the Eval monad, Evaluation Strategies, and the Par monad. On the concurrent side, we cover threads, MVars, asynchronous exceptions, Software Transactional Memory, the Foreign Function Interface, and briefly look at the construction of high-speed network servers in Haskell.

In 1996, Gil and Lorenz proposed programming language constructs for specifying environmental acquisition in addition to inheritance acquisition for objects. They noticed that in many programs, objects are arranged in containment hierarchies and need to obtain information from their container objects. Therefore, if languages allowed programmers to specify such relationships directly, type systems and runtime environments could enforce the invariants that make these programming patterns work.In this paper, we present a formal version of environmental acquisition for class-based languages. Specifically, we introduce an extension of the ClassicJava model with constructs for environmental acquisition of fields and methods, a type system for the model, a reduction semantics, and a type soundness proof. We also discuss how to scale the model to a full-scale Java-like programming language.