Modern graphics processing units (GPUs) have complex architectures that admit
exceptional performance and energy efficiency for high-throughput applications. Although …

Accurate automatic optimization heuristics are necessary for dealing with thecomplexity and
diversity of modern hardware and software. Machine learning is aproven technique for …

Due to decelerating gains in single-core CPU performance, computationally expensive
simulations are increasingly executed on highly parallel hardware platforms. Agent-based …

Graphics Processing Units (GPUs) have numerous configuration and design options,
including core frequency, number of parallel compute units (CUs), and available memory …

Emergent heterogeneous systems must be optimized for both power and performance at
exascale. Massive parallelism combined with complex memory hierarchies form a barrier to …

Edge devices are seeing tremendous growth in sensing and computational capabilities.
Running state-of-the-art deep neural network (NN) based data processing on multi-core …

Graphics processors, or GPUs, have recently been widely used as accelerators in shared
environments such as clusters and clouds. In such shared environments, many kernels are …

Modern commodity processors such as GPUs may execute up to about a thousand of
physical threads per chip to better utilize their numerous execution units and hide execution …

The gap between CPU and networking speeds has motivated the development of
SmartNICs for NF (network functions) offloading. However, offloading performance is …

Recently, FPGA vendors such as Altera and Xilinx have released OpenCL SDK for
programming FPGAs. However, the architecture of FPGA is significantly different from that of …