Automatically deriving cost models for structured parallel processes using hylomorphisms

This work has been partially supported by the EU Horizon 2020 grant “RePhrase: Refactoring Parallel Heterogeneous Resource-Aware Applications - a Software Engineering Approach” (ICT-644235), by COST Action IC1202 (TACLe), supported by COST (European Cooperation on Science and Technology), and by EPSRC grant EP/M027317/1 “C33: Scalable & Verified Shared Memory via Consistency-directed Cache Coherence”.Structured parallelism using nested algorithmic skeletons can greatly ease the task of writing parallel software, since common, but hard-to-debug, problems such as race conditions are eliminated by design. However, choosing the best combination of algorithmic skeletons to yield good parallel speedups for a specific program on a specific parallel architecture is still a difficult problem. This paper uses the unifying notion of hylomorphisms, a general recursion pattern, to make it possible to reason about both the functional correctness properties and the extra-functional timing properties of structured parallel programs. We have previously used hylomorphisms to provide a denotational semantics for skeletons, and proved that a given parallel structure for a program satisfies functional correctness. This paper expands on this theme, providing a simple operational semantics for algorithmic skeletons and a cost semantics that can be automatically derived from that operational semantics. We prove that both semantics are sound with respect to our previously defined denotational semantics. This means that we can now automatically and statically choose a provably optimal parallel structure for a given program with respect to a cost model for a (class of) parallel architecture. By deriving an automatic amortised analysis from our cost model, we can also accurately predict parallel runtimes and speedups.PostprintPeer reviewe

Computing Downwards Accumulations on Trees Quickly

Downwards accumulations on binary trees are essentially functions which pass information down a tree. Under certain conditions, these accumulations are both `efficient' (computable in a functional style in parallel time proportional to the depth of the tree) and `manipulable'. In this paper, we show that these conditions do in fact yield a stronger conclusion: the accumulation can be computed in parallel time proportional to the logarithm of the depth of the tree, on a CREW PRAM machine

Computing Downwards Accumulations on Trees Quickly

Downwards passes\\/ on binary trees are essentially functions which pass information down a tree, from the root towards the leaves. Under certain conditions, a downwards pass is both `efficient' (computable in a functional style in parallel time proportional to the depth of the tree) and `manipulable' (enjoying a number of distributivity properties useful in program construction); we call a downwards pass satisfying these conditions a downwards accumulation. In this paper, we show that these conditions do in fact yield a stronger conclusion: the accumulation can be computed in parallel time proportional to the logarithm\\/ of the depth of the tree, on a CREW PRAM machine

Computing Downwards Accumulations on Trees Quickly

Downwards passes on binary trees are essentially functions which pass information down a tree, from the root towards the leaves. Under certain conditions, a downwards pass is both `efficient' (computable in a functional style in parallel time proportional to the depth of the tree) and `manipulable' (enjoying a number of distributivity properties useful in program construction); we call a downwards pass satisfying these conditions a downwards accumulation. In this paper, we show that these conditions do in fact yield a stronger conclusion: the accumulation can be computed in parallel time proportional to the logarithm of the depth of the tree, on a Crew Pram machine