CppCon 2020 A Parallel and Heterogeneous Task Programming System Using Modern C++--Tsung-Wei Huang

Registration is now open for CppCon 2021, which starts on October 24 and will be held both in person and online. To whet your appetite for this year’s conference, we’re posting videos of some of the top-rated talks from our most recent in-person conference in 2019 and our online conference in 2020. Here’s another CppCon talk video we hope you will enjoy – and why not register today for CppCon 2021 to attend in person, online, or both!

A Parallel and Heterogeneous Task Programming System Using Modern C++

by Tsung-Wei Huang

Summary of the talk:

The Taskflow project addresses the long-standing question: "How can we make it easier for developers to write parallel and heterogeneous programs with high performance and simultaneous high productivity?" Modern scientific computing relies on a heterogeneous mix of computational patterns, domain algorithms, and specialized hardware to achieve key scientific milestones that go beyond traditional capabilities. However, programming these applications often requires complex expert-level tools and a deep understanding of software methodologies. Specifically, the lack of a suitable software environment that can overcome the complexity of programming large parallel and heterogeneous systems has posed a significant barrier for many organizations to facilitate transformational discoveries.

Taskflow develops a simple and powerful task programming model to enable efficient implementations of heterogeneous decomposition strategies. Our programming model empowers users with both static and dynamic task graph constructions to incorporate a broad range of computational patterns including hybrid CPU-GPU computing, dynamic control flow, and irregularity. We develop an efficient heterogeneous work-stealing strategy that adapts worker threads to available task parallelism at any time during the graph execution. We have demonstrated promising performance of Taskflow on both micro-benchmark and real-world applications. As an example, we solved a large machine learning workload by up to 1.5× faster, 1.6× less memory, and 1.7× fewer lines of code than two industrial-strength systems, oneTBB and StarPU, on a machine of 40 CPUs and 4 GPUs.

This talk will cover three aspects: (1) heterogeneous task programming model using modern C++, (2) an efficient work-stealing strategy generalizable to arbitrary heterogeneous domains, and (3) user experience we have obtained and suggested roadmap for C++ in face of future heterogeneity.

The Taskflow project is available at https://taskflow.github.io/

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