FastFlow: Efficient Parallel Streaming Applications on Multi-core

TL;DR

FastFlow is a low-level programming framework that significantly improves the efficiency of streaming applications on multi-core shared memory architectures, outperforming existing frameworks in micro-benchmarks and real-world tasks.

Contribution

Introducing FastFlow, a lock-free queue-based framework optimized for streaming applications on multi-core architectures, with demonstrated superior performance over Cilk, OpenMP, and TBB.

Findings

FastFlow outperforms Cilk, OpenMP, and TBB in micro-benchmarks.

FastFlow achieves up to +226% speedup in real-world protein alignment tasks.

FastFlow is more efficient for fine-grain tasks on multi-core systems.

Abstract

Shared memory multiprocessors come back to popularity thanks to rapid spreading of commodity multi-core architectures. As ever, shared memory programs are fairly easy to write and quite hard to optimise; providing multi-core programmers with optimising tools and programming frameworks is a nowadays challenge. Few efforts have been done to support effective streaming applications on these architectures. In this paper we introduce FastFlow, a low-level programming framework based on lock-free queues explicitly designed to support high-level languages for streaming applications. We compare FastFlow with state-of-the-art programming frameworks such as Cilk, OpenMP, and Intel TBB. We experimentally demonstrate that FastFlow is always more efficient than all of them in a set of micro-benchmarks and on a real world application; the speedup edge of FastFlow over other solutions might be bold for fine grain tasks, as an example +35% on OpenMP, +226% on Cilk, +96% on TBB for the alignment of protein P01111 against UniProt DB using Smith-Waterman algorithm.