We design and develop ObliVM, a programming framework for secure computation. ObliVM
offers a domain specific language designed for compilation of programs into efficient …

The rapidly growing popularity and scale of data-parallel workloads demand a
corresponding increase in raw computational power of Graphics Processing Units (GPUs) …

GPUs achieve high throughput and power efficiency by employing many small single
instruction multiple thread (SIMT) cores. To minimize scheduling logic and performance …

Exploiting data locality in GPUs is critical to making more efficient use of the existing caches
and the NUMA-based memory hierarchy expected in future GPUs. While modern GPU …

As transistor scaling becomes increasingly more difficult to achieve, scaling the core count
on a single GPU chip has also become extremely challenging. As the volume of data to …

Recently, a large number of convolutional neural network (CNN) inference services have
emerged on high-performance Graphic Processing Units (GPUs). However, GPUs are high …

Emerging mobile apps such as UHD video and AR/VR access diverse high-throughput
hardware devices, eg, video codecs, cameras, and image processors. However, today's …

Heterogeneous computing with accelerators is growing in importance in high performance
computing (HPC). Recently, application datasets have expanded beyond the memory …

To exploit parallelism and scalability of multiple GPUs in a system, it is critical to place
compute and data together. However, two key techniques that have been used to hide …

Graphics Processing Unit (GPU) performance has relied heavily on our ability to scale of
number of transistors on chip, in order to satisfy the ever-increasing demands for more …