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This paper presents some preliminary results on a small light weight user level task management library called Wool. The Wool task scheduler is based on work stealing. The objective of the library is to provide a reasonably convenient programming interface (in particular by not forcing the programmer to write in continuation passing style) in ordinary C while still having a very low task creation overhead. Several task scheduling systems based on work stealing exists, but they are typically either programming languages like Cilk-5 or based on C++ like the Intel TBB or C# as in the Microsoft TPL. Our main conclusions are that such a direct style interface is indeed possible and yields performance that is comparable to that of the Intel TBB.

This paper gives a static semantics for Haskell 98, a non-strict purely functional programming language. The semantics formally specifies nearly all the details of the Haskell 98 type system, including the resolution of overloading, kind inference (including defaulting) and polymorphic recursion, the only major omission being a proper treatment of ambiguous overloading and its resolution. Overloading is translated into explicit dictionary passing, as in all current implementations of Haskell. The target language of this translation is a variant of the Girard–Reynolds polymorphic lambda calculus featuring higher order polymorphism and explicit type abstraction and application in the term language. Translated programs can thus still be type checked, although the implicit version of this system is impredicative. A surprising result of this formalization effort is that the monomorphism restriction, when rendered in a system of inference rules, compromises the principal type property.

With the proliferation of multicore processors, there is an urgent need for tools and methodologies supporting parallelization of existing applications. In this paper, we present a novel tool for aiding programmers in parallelizing programs. The tool, Embla, is based on the Valgrind framework, and allows the user to discover the data dependences in a sequential program, thereby exposing opportunities for parallelization. Embla performs an off-line dynamic analysis, and records dependences as they arise during program execution. It reports an optimistic view of parallelizable sequences, and ignores dependences that do not arise during execution. Moreover, since the tool instruments the machine code of the program, it is largely language independent.Since Embla finds the dependencies that occur for particular executions, the confidence one would assign to its results depend on whether different executions yield different (bad) or largely the same (good) dependencies. We present a preliminary investigation into this issue using 84 different inputs to the SPEC CPU2006 benchmark 403.gcc. The results indicate that there is a strong correlation between coverage and finding dependencies; executing the entire program is likely to reveal all dependencies.