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September 28, 2007

Language Design for an Uncertain Hardware Future

by Roy Lurie, The MathWorks

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Do you know where your algorithms will be running two years from now? Five? Ten? Are you investing in code today that you will need to throw away? What language should you choose today for your algorithms, to protect your investment for the future?

Every industry faces increasing interest and need for high performance computing. From automotive simulation to financial risk modeling, to systems biology and communication systems design, the need for raw computing power has increased dramatically, and will continue to do so. With the advent of high performance computing, hardware will provide the platform needed for this work.

Some fear that the explosion of diversity in hardware architectures means that the hardware available today will be replaced by something faster and better just beyond the horizon. This has always been happening, but the "C" single processor model remained while the processor architecture evolved. In the world of FPGAs, GPGPUs, many-cores, accelerators, multicores, clusters, grids, Cell processors and reconfigurable hardware, this model is not working. How do you choose a strategy that insulates you from these changes?

Many organizations have algorithm intellectual property locked into a particular language or environment that makes it virtually impossible to migrate to new technology. Often the experts who understand the subtleties of these codes and the particular optimizations made to get the "best performance" are not around anymore. With an uncertain future, prematurely selecting your architecture, language, and algorithm will require you to, at best, invest heavily in migrating the code, or at worst, live with legacy systems beyond their useful life.

So, what should software developers and domain experts be demanding from language providers to reduce the risk of algorithm obsolescence?

A best practice in software engineering is, where possible, to write a program in the simplest way that is the easiest to understand and maintain. Don't try to predict where the performance bottlenecks are going to be in the first pass. Once the algorithm is working correctly, run it to find the performance bottlenecks. Trying to optimize for performance before you have the right algorithm leads to speculative performance enhancements that make the code less readable and maintainable, and that doesn't address the underlying performance issues because you guessed incorrectly. This article applies this logic to language design for the future of high performance systems.

Languages should allow domain experts to develop the right algorithm as quickly as possible, without worrying initially about architectural nuances. To optimize your long-term investment in algorithms, you need to be able to express the algorithm in the highest level of abstraction possible, without prematurely adding architecture- or system-specific constructs.

In this two-pass model, domain experts, like the scientists and engineers who will be major consumers of high performance computing systems, should be able to express their ideas in a natural way, allowing them to explore their solution space rapidly. To maximize their productivity, these experts should be able to focus on their core competencies. For example, image processing experts should have at hand a language whose semantics, syntax and functions match the domain's normal expression of ideas. Allowing image processing experts to remain focused on the core algorithm concepts, rather than the mundane issues of memory allocation, threading or data handling, empowers them to rapidly create appropriate algorithms.

The second pass of this two-pass model is the ability for users to annotate the algorithm with additional information that will act as guides and input to the underlying execution engine in order to achieve optimal performance for a particular architecture. This might include annotations to describe parallelism in the algorithm. Clearly, there are situations where architecture drives algorithms, and a distinct two-pass model is infeasible.

A better approach would be for the language to require no annotation to make optimal use of a particular architecture. This "fully implicit" system requires only a single pass performing operations such as automatic parallelization. This is an active research area, with currently no general solution. So for the foreseeable future, some annotation will be needed to provide clues to the particular execution engine to perform optimally. Such a system can be described as "minimally explicit," where the minimum amount of explicit information is needed to assist the execution engine in producing optimal performance.

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