Simplifying programming and load balancing of data parallel applications on heterogeneous systems

Heterogeneous architectures have experienced a great development thanks to their excellent cost/performance ratio and low power consumption. But heterogeneity significantly complicates both programming and efficient use of the resources. As a result, programmers have ended up using fixed roles for each kind of device: CPUs for sequential and management tasks and GPUs for parallel work. This is a waste of computing power. Maat is a library for OpenCL programmers that allows for the efficient execution of a single data-parallel kernel using all the available devices. It provides the programmer with an abstract view of the system to enable the management of heterogeneous environments regardless of the underlying architecture, and a set of load balancing methods, which perform data distribution. With Maat, programmers only need to develop a data-parallel kernel, select a load balancing method, and run it on the whole system. Experimental results show that Maat efficiently utilizes all the resources, independently of their number and nature. Provided the most appropriate method is selected, Maat is able to achieve a speedup of up to 1.97 using two GPUs with respect to a single GPU and even over 2 when the CPUs, which are much less performant, come into play.