Yash Ukidave

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—Heterogeneous computing using Graphic Processing Units (GPUs) has become an attractive computing model given the available scale of data-parallel performance and programming standards such as OpenCL. However, given the energy issues present with GPUs, some devices can exhaust power budgets quickly. Better solutions are needed to effectively exploit the(More)
Heterogeneous systems have grown in popularity within the commercial platform and application developer communities. We have seen a growing number of systems incorporating CPUs, Graphics Processors (GPUs) and Accelerated Processing Units (APUs combine a CPU and GPU on the same chip). These emerging class of platforms are now being targeted to accelerate(More)
Heterogeneous systems consisting of multi-core CPUs, Graphics Processing Units (GPUs) and many-core accelerators have gained widespread use by application developers and data-center platform developers. Modern day heterogeneous systems have evolved to include advanced hardware and software features to support a spectrum of application patterns.(More)
Heterogeneous computing has become prevalent due to the computing power and low cost of Graphics Processing Units(GPUs). OpenCL provides a programming model where the CPU is the master or host, and compute-intensive portions of an algorithm are offloaded to the GPU. However, the host-device model is very limiting. In this model, data-dependent, run-time(More)
Graphics Processing Units (GPUs) have gained recognition as the primary form of accelerators for graphics rendering in the gaming domain. They have also been widely accepted as the computing platform of choice in many scientific and high performance computing domains. The parallelism offered by the GPUs is used for simultaneous processing of compute and(More)
As we move into a new era of heterogeneous multi-core systems, our ability to tune the performance and understand the reliability of both hardware and software becomes more challenging. Given the multiplicity of different design trade-offs in hardware and software, and the rate of introduction of new architectures and hardware/software features, it becomes(More)
The growth in demand for heterogeneous accelerators has stimulated the development of cutting-edge features in newer accelerators. The heterogeneous programming frameworks such as OpenCL have matured over the years and have introduced new software features for developers. We explore one of these programming frameworks, OpenCL 2.0. To drive our study, we(More)