challenging facet of software engineering—it can be thought of as akin to a complex version of the problem of resource management. Parallel-program productivity can be improved if we can develop languages that provide useful software-engineering abstractions for parallelism, parallel components and libraries that programmers can reuse, and a software-hardware stack that can facilitate reasoning about all of them.


The need for robust, general, and scalable parallel-software approaches presents challenges that affect the entire computing ecosystem. There are numerous possible paths toward a future that exploits abundant parallelism while managing locality. Parallel programming and parallel computers have been around since the 1960s, and much progress has been made. Much of the challenge of parallel programming deals with making parallel programs efficient and portable without requiring heroic efforts on the part of the programmer. No subfield or niche will be able to solve the problem of sustaining growth in computing performance on its own. The uncertainty about the best way forward is inhibiting investment. In other words, there is currently no parallel-programming approach that can help drive hardware development. Historically, a vendor might have taken on risk and invested heavily in developing an ecosystem, but given all the uncertainty, there is not enough of this investment, which entails risk as well as innovation. Research investment along multiple fronts, as described in this report, is essential.25

Software lasts a long time. The huge entrenched base of legacy software is part of the reason that people resist change and resist investment in new models, which may or may not take advantage of the capital investment represented by legacy software. Rewriting software is expensive. The economics of software results in pressure against any kind of innovative models and approaches. It also explains why the approaches we have seen have had relatively narrow applications or been incremental. Industry, for example, has turned to chip multiprocessors (CMPs) that replicate existing cores a few times (many times in the future). Care is taken to maintain backward compatibility to bring forward the existing multi-billion-dollar installed software base. With prospects dim for


25A recent overview in Communications of the ACM articulates the view that developing software for parallel cores needs to become as straightforward as writing software for traditional processors: Krste Asanovic, Rastislav Bodik, James Demmel, Tony Keaveny, Kurt Keutzer, John Kubiatowicz, Nelson Morgan, David Patterson, Koushik Sen, John Wawrzynek, David Wessel, and Katherine Yelick, 2009, A view of the parallel computing landscape, Communications of the ACM 52(10): 56-67, available online at

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