Congratulations to Naila Farooqui on successfully defending her thesis “Runtime Specialization for Heterogeneous CPU-GPU Platforms”!

Posted by on Oct 23, 2015 in News

Congratulations to Naila Farooqui on successfully defending her thesis “Runtime Specialization for Heterogeneous CPU-GPU Platforms”!

Naila Farooqui successfully defended her thesis on Oct 19, 2015. Congratulations Dr. Farooqui!

The abstract of Naila’s thesis goes as follows:

Heterogeneous parallel architectures like those comprised of CPUs and GPUs are a tantalizing compute fabric for performance-hungry developers. While these platforms enable order-of-magnitude performance increases for many data-parallel application domains, there remain several open challenges: (i) the distinct execution models inherent in the heterogeneous devices present on such platforms drive the need to dynamically match workload characteristics to the underlying resources, (ii) the complex architecture and programming models of such systems require substantial application knowledge and effort-intensive program tuning to achieve high performance, and (iii) as such platforms become prevalent, there is a need to extend their utility from running known regular data-parallel applications to the broader set of input-dependent, irregular applications common in enterprise settings.

The key contribution of our research is to enable runtime specialization on such hybrid CPU-GPU platforms by matching application characteristics to the underlying heterogeneous resources for both regular and irregular workloads. Our approach enables profile-driven resource management and optimizations for such platforms, providing high application performance and system throughput. Towards this end, this research will: (a) enable dynamic instrumentation for GPU-based parallel architectures, specifically targeting the complex Single-Instruction Multiple-Data (SIMD) execution model, to gain real-time introspection into application behavior; (b) leverage such dynamic performance data to support novel online resource management methods that improve application performance and system throughput, particularly for irregular, input-dependent applications; (c) automate some of the programmer effort required to exercise specialized architectural features of such platforms via
instrumentation-driven dynamic code optimizations; and (d) propose a specialized, affinity-aware work-stealing scheduler for integrated CPU-GPU processors that efficiently distributes work at runtime across all CPU and GPU cores for improved load balance, taking into account both application characteristics and architectural differences of the underlying devices.