Data-aware scheduling of legacy kernels on heterogeneous platforms with distributed memory

In this paper, we describe a runtime to automatically enhance the performance of applications running on heterogeneous platforms consisting of a multi-core (CPU) and a throughput-oriented many-core (GPU). The CPU and GPU are connected by a non-coherent interconnect such as PCI-E, and as such do not have shared memory. Heterogeneous platforms available today such as [9] are of this type. Our goal is to enable the programmer to seamlessly use such a system without rewriting the application and with minimal knowledge of the underlying architectural details. Assuming that applications perform function calls to computational kernels with available CPU and GPU implementations, our runtime achieves this goal by automatically scheduling the kernels and managing data placement. In particular, it intercepts function calls to well-know