Martin Elsman

A Retrospective on Region-Based Memory Management

We report on our experience with designing, implementing, proving correct, and evaluating a region-based memory management system.

Compiling a Subset of APL Into a Typed Intermediate Language

We present a compiler and a typed intermediate language for a subset of APL. The intermediate language treats all numeric data as multi-dimensional arrays and the type system makes explicit the ranks of arrays. Primitive operators are polymorphic in rank and in the type of the underlying data operated on. The frontend of the APL compiler deals with much of the gory details of the APL language, including infix resolution, resolution of identity items for reduce operations, resolution of default element values for empty arrays, scalar extensions, and resolution of certain kinds of overloading. We demonstrate the usefulness of the intermediate language by showing that it can be compiled efficiently, using known techniques, such as delayed arrays, into a C-like language. We also demonstrate that the language is sufficiently expressive that some primitive operators, such as APLs inner product operator, which works on arrays of arbitrary dimensions, can be compiled using more primitive operators.

Futhark: purely functional GPU-programming with nested parallelism and in-place array updates

Futhark is a purely functional data-parallel array language that offers a machine-neutral programming model and an optimising compiler that generates OpenCL code for GPUs. This paper presents the design and implementation of three key features of Futhark that seek a suitable middle ground with imperative approaches. First, in order to express efficient code inside the parallel constructs, we introduce a simple type system for in-place updates that ensures referential transparency and supports equational reasoning. Second, we furnish Futhark with parallel operators capable of expressing efficient strength-reduced code, along with their fusion rules. Third, we present a flattening transformation aimed at enhancing the degree of parallelism that (i) builds on loop interchange and distribution but uses higher-order reasoning rather than array-dependence analysis, and (ii) still allows further locality-of-reference optimisations. Finally, an evaluation on 16 benchmarks demonstrates the impact of the language and compiler features and shows application-level performance competitive with hand-written GPU code.

Size slicing: a hybrid approach to size inference in futhark

We present a shape inference analysis for a purely-functional language, named Futhark, that supports nested parallelism via array combinators such as map, reduce, filter}, and scan}. Our approach is to infer code for computing precise shape information at run-time, which in the most common cases can be effectively optimized by standard compiler optimizations. Instead of restricting the language or sacrificing ease of use, the language allows the occasional shape-dynamic, and even shape-misbehaving, constructs. Inherently shape-dynamic code is treated with a fall-back technique that preserves, asymptotically, the number of operations of the program and that computes and returns the arrays shape alongside with its value. This approach leads to a shape-dependent system with existentially-quantified types, where static shape inference corresponds to eliminating existential quantifications from the types of program expressions. We optimize the common case to negligible overhead via size slicing: a technique that separates the computation of the arrays shape from its values. This allows the shape to be calculated in advance and to be used to instantiate the previously existentially-quantified shapes of the value slice. We report negligible overhead, on several mini-benchmarks and three real-world applications.

Certified symbolic management of financial multi-party contracts

Domain-specific languages (DSLs) for complex financial contracts are in practical use in many banks and financial institutions today. Given the level of automation and pervasiveness of software in the sector, the financial domain is immensely sensitive to software bugs. At the same time, there is an increasing need to analyse (and report on) the interaction between multiple parties. In this paper, we present a multi-party contract language that rigorously relegates any artefacts of simulation and computation from its core, which leads to favourable algebraic properties, and therefore allows for formalising domain-specific analyses and transformations using a proof assistant. At the centre of our formalisation is a simple denotational semantics independent of any stochastic aspects. Based on this semantics, we devise certified contract analyses and transformations. In particular, we give a type system, with an accompanying type inference procedure, that statically ensures that contracts follow the principle of causality. Moreover, we devise a reduction semantics that allows us to evolve contracts over time, in accordance with the denotational semantics. From the verified Coq definitions, we automatically extract a Haskell implementation of an embedded contract DSL along with the formally verified contract management functionality. This approach opens a road map towards more reliable contract management software, including the possibility of analysing contracts based on symbolic instead of numeric methods.

Polymorphic Equality - No Tags Required

Polymorphic equality is a controversial language construct. While being convenient for the programmer, it has been argued that polymorphic equality (1) invites to violation of software engineering principles, (2) lays a serious burden on the language implementor, and (3) enforces a runtime overhead due to the necessity of tagging values at runtime. We show that neither (2) nor (3) are inherent to polymorphic equality by showing that one can compile programs with polymorphic equality into programs without polymorphic equality in such a way that there is no need for tagging or for runtime type analysis. Also, the translation is the identity on programs that do not use polymorphic equality. Experimental results indicate that even for programs that use polymorphic equality, the translation gives good results.

APL on GPUs: a TAIL from the past, scribbled in Futhark

This paper demonstrates translation schemes by which programs written in a functional subset of APL can be compiled to code that is run efficiently on general purpose graphical processing units (GPGPUs). Furthermore, the generated programs can be straightforwardly interoperated with mainstream programming environments, such as Python, for example for purposes of visualization and user interaction. Finally, empirical evaluation shows that the GPGPU translation achieves speedups up to hundreds of times faster than sequential C compiled code.

FinPar: A Parallel Financial Benchmark

Commodity many-core hardware is now mainstream, but parallel programming models are still lagging behind in efficiently utilizing the application parallelism. There are (at least) two principal reasons for this. First, real-world programs often take the form of a deeply nested composition of parallel operators, but mapping the available parallelism to the hardware requires a set of transformations that are tedious to do by hand and beyond the capability of the common user. Second, the best optimization strategy, such as what to parallelize and what to efficiently sequentialize, is often sensitive to the input dataset and therefore requires multiple code versions that are optimized differently, which also raises maintainability problems. This article presents three array-based applications from the financial domain that are suitable for gpgpu execution. Common benchmark-design practice has been to provide the same code for the sequential and parallel versions that are optimized for only one class of datasets. In comparison, we document (1) all available parallelism via nested map-reduce functional combinators, in a simple Haskell implementation that closely resembles the original code structure, (2) the invariants and code transformations that govern the main trade-offs of a data-sensitive optimization space, and (3) report target cpu and multiversion gpgpu code together with an evaluation that demonstrates optimization trade-offs and other difficulties. We believe that this work provides useful insight into the language constructs and compiler infrastructure capable of expressing and optimizing such applications, and we report in-progress work in this direction.

Typelets A Rule-Based Evaluation Model for Dynamic, Statically Typed User Interfaces

We present the concept of typelets, a specification technique for dynamic graphical user interfaces (GUIs) based on types. The technique is implemented in a dialect of ML, called MLFi, which supports dynamic types, for migrating type-level information into the object level, so-called type properties, allowing easy specification of, for instance, GUI control attributes, and type paths, which allows for type-safe access to type components at runtime. Through the use of Hindley-Milner style type-inference in MLFi, the features allow for type-level programming of user interfaces. The dynamic behavior of typelets are specified using declarative rules. The technique extends the flat spreadsheet programming model with higher-order rule composition techniques, extensive reuse, and type safety. A layout specification language allows layout programmers (e.g., end-users) to reorganize layouts in a type-safe way without being allowed to alter the rule machinery. The resulting framework is highly flexible and allows for creating highly maintainable modules. It is used with success in the context of SimCorps high-end performance-critical financial asset-management system with screens containing several hundreds of GUI controls located in group-boxes, sub-tabs, and menu structures and with very complex dependency structures defined using declarative rule composition.

Certified Compilation of Financial Contracts

We present an extension to a certified financial contract management system that allows for templated declarative financial contracts and for integration with financial stochastic models through verified compilation into so-called payoff-expressions. Such expressions readily allow for determining the value of a contract in a given evaluation context, such as contexts created for stochastic simulations. The templating mechanism is useful both at the contract specification level, for writing generic reusable contracts, and for reuse of code that, without the templating mechanism, needs to be recompiled for different evaluation contexts. We report on the effect of using the certified system in the context of a GPGPU-based Monte Carlo simulation engine for pricing various over-the-counter (OTC) financial contracts. The full contract-management system, including the payoff-language compilation, is verified in the Coq proof assistant and certified Haskell code is extracted from our Coq development along with Futhark code for use in a data-parallel pricing engine.