PetaBricks

Building adaptable and more efficient programs
for the multi-core era is now within reach.
By Jason Ansel and Cy Chan
DOI: 10.1145/1836543.1836554

With the dawn of the multi-core era, programmers are being challenged to write code that performs well on an increasingly diverse array of architectures. A single program or library may be used on systems ranging in power from large servers with dozens or hundreds of cores to small single-core netbooks or mobile phones. A program may need to run efficiently both on architectures with many simple cores and on those with fewer monolithic cores. Some of the systems a program encounters might have GPU coprocessors, while others might not. Looking forward, processor designs such as asymmetric multi-core [ 3], with different types of cores on a single chip, will present an even greater challenge for programmers to utilize effectively.

that allows the user to specify algorithmic choices at the language level. Using this mechanism, PetaBricks programs define not a single algorithmic path, but a search space of possible paths. This flexibility allows our compiler to build programs that can automatically

“Peta Bricks
programs define
not a single
algorithmic path,
but a search space of
possible paths. This
flexibility allows our
compiler to build
programs that can
automatically adapt,
with empirical
autotuning, to every
architecture they
encounter.”

adapt, with empirical autotuning, to every architecture they encounter.

PE TABRICKS LANGUAGE

The PetaBricks language provides a framework for the programmer to describe multiple ways of solving a problem while allowing the autotuner to determine which of those ways is best for the user’s situation. It provides both algorithmic flexibility (multiple algorithmic choices) as well as coarse-grained code generation flexibility (synthesized outer control flow).

At the highest level, the programmer can specify a transform, which takes some number of inputs and produces some number of outputs. In this respect, the PetaBricks transform is like a function call in any common procedural language. The major difference with PetaBricks is that we allow the programmer to specify multiple pathways to convert the inputs to the outputs for each transform. Pathways are specified in a dataflow manner using a number of smaller building blocks called rules, which encode both the data dependencies of the rule and C++-like code that converts the rule’s inputs to outputs. Dependencies are specified