Isaac Gelado, John E. Stone, Javier Cabezas, Sanjay Patel, Nacho Navarro, and
Wen-mei W. Hwu.
An asymmetric distributed shared memory model for heterogeneous
parallel system.
In ASPLOS '10: Proceedings of the 15th International Conference
on Architectural Support for Programming Languages and Operating Systems,
pp. 347-358, 2010.
GELA2010-JS
Heterogeneous computing combines general purpose CPUs with accelerators to efficiently
execute both sequential control-intensive and data-parallel phases of applications.
Existing programming models for heterogeneous computing rely on programmers to
explicitly manage data transfers between the CPU system memory and accelerator
memory.
This paper presents a new programming model for heterogeneous computing, called
Asymmetric Distributed Shared Memory (ADSM), that maintains a shared logical memory
space for CPUs to access objects in the accelerator physical memory but not vice versa.
The asymmetry allows light-weight implementations that avoid common pitfalls of
symmetrical distributed shared memory systems. ADSM allows programmers to assign
data objects to performance critical methods. When a method is selected for accelerator
execution, its associated data objects are allocated within the shared logical memory
space, which is hosted in the accelerator physical memory and transparently accessible by
the methods executed on CPUs.
We argue that ADSM reduces programming efforts for heterogeneous computing systems
and enhances application portability. We present a software implementation of ADSM,
called GMAC, on top of CUDA in a GNU/Linux environment. We show that applications
written in ADSM and running on top of GMAC achieve performance comparable to their
counterparts using programmer-managed data transfers. This paper presents the GMAC
system and evaluates different design choices. We further suggest additional architectural
support that will likely allow GMAC to achieve higher application performance than the
current CUDA model.
