As both CPUs and GPUs become employed in a wide range of applications, it has been
acknowledged that both of these Processing Units (PUs) have their unique features and …

Computer systems increasingly rely on heterogeneity to achieve greater performance,
scalability and energy efficiency. Because heterogeneous systems typically comprise …

This work presents CLTune, an auto-tuner for OpenCL kernels. It evaluates and tunes kernel
performance of a generic, user-defined search space of possible parameter-value …

A very common problem in GPU programming is that some combination of thread block
dimensions and other code optimization parameters, like tiling or unrolling factors, results in …

Across embedded, mobile, enterprise, and high performance computing systems, computer
architectures are becoming more heterogeneous and complex. This complexity is causing a …

This paper introduces a new resource virtualization framework, Zorua, that decouples the
programmer-specified resource usage of a GPU application from the actual allocation in the …

Heterogeneous architectures have experienced a great development thanks to their
excellent cost/performance ratio and low power consumption. But heterogeneity significantly …

A challenge that heterogeneous system programmers face is leveraging the performance of
all the devices that integrate the system. This paper presents Sigmoid, a new load balancing …

Finding optimal parameter configurations for tunable GPU kernels is a non-trivial exercise
for large search spaces, even when automated. This poses an optimization task on a …

From edge to exascale, computer architectures are becoming more heterogeneous and
complex. The systems typically have fat nodes, with multicore CPUs and multiple hardware …