2021 Proceeding- General Chairs:
- Tze Meng Low,
- Jeremy Gibbons
Welcome to the Seventh ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming, held in association with PLDI 2021. Array-oriented programming offers a unique blend of programmer productivity and high-performance parallel execution. As an abstraction, it directly mirrors high-level mathematical constructions commonly used in many fields from natural sciences through engineering to financial modelling. As a language feature, it exposes regular control flow, exhibits structured data dependencies, and lends itself to many types of program analysis. Furthermore, many modern computer architectures, particularly highly parallel architectures such as GPUs and FPGAs, lend themselves to efficiently executing array operations. The ARRAY workshop series is intended to bring together researchers from many different communities, including language designers, library developers, compiler researchers, and practitioners, who are using or working on numeric, array-centric aspects of programming languages, libraries and methodologies from all domains: imperative or declarative; object-oriented or functional; interpreted or compiled; strongly typed, weakly typed, or untyped.
Proceeding Downloads
Towards size-dependent types for array programming
We present a type system for expressing size constraints on array types in an ML-style type system. The goal is to detect shape mismatches at compile-time, while being simpler than full dependent types. The main restrictions is that the only terms that ...
Padding in the mathematics of arrays
Multi-dimensional array manipulation constitutes a core component of numerous numerical methods, e.g. finite difference solvers of Partial Differential Equations (PDEs). The efficiency of such computations is tightly connected to traversing array data ...
Acceleration of lattice models for pricing portfolios of fixed-income derivatives
This paper reports on the acceleration of a standard, lattice-based numerical algorithm that is widely used in finance for pricing a class of fixed-income vanilla derivatives.
We start with a high-level algorithmic specification, exhibiting irregular ...
Array languages make neural networks fast
Most implementations of machine learning algorithms are based on special-purpose frameworks such as TensorFlow or PyTorch. While these frameworks are convenient to use, they introduce multi-million lines of code dependency that one has to trust, ...
Index Terms
- Proceedings of the 7th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming
Recommendations
Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| ARRAY'14 | 25 | 17 | 68% |
| Overall | 25 | 17 | 68% |