practice

Doi: 10.1145/1978542.1978558

article development led by queue.acm.org

Heterogeneous systems allow us to target our
programming to the appropriate environment.
BY satnam sinGh
Computing
Without
Processors

FrOM the PrOGraMMer’s perspective the distinction between hardware and software is being blurred.

As programmers struggle to meet the performance requirements of today’s systems they will face an ever increasing need to exploit alternative computing elements such as graphics processing units (GPUs), which are graphics cards subverted for data-parallel computing, 11 and field-programmable gate arrays (FPGAs), or soft hardware.

The current iteration of mainstream computing architectures is based on cache-coherent multicore processors. Variations on this theme include Intel’s experimental Single-Chip Cloud Computer, which contains 48 cores that are not cache coherent. This path, however, is dictated by the end of frequency scaling rather than being driven by requirements about how programmers wish to write software. 4 The conventional weapons available for writing concurrent and parallel software for such multicore

systems are largely based on abstractions developed for writing operating systems (for example, locks and monitors). However, these are not the correct abstractions to use for writing parallel applications.

There are better ways to bake all that sand. Rather than composing many elements that look like regular CPUs, a better approach, from a latency and energy-consumption perspective, is to use a diverse collection of processing elements working in concert and tuned to perform different types of computation and communication. Large coarse-grain tasks are suitable for implementation on multicore processors. Thousands of fine-grain data-parallel computations are suitable for implementation on GPUs. Irregular