The Single Instruction Multiple Data (SIMD) architecture, supported by various high-
performance computing platforms, efficiently utilizes data-level parallelism. The SIMD model …

Across embedded, mobile, enterprise, and high performance computing systems, computer
architectures are becoming more heterogeneous and complex. This complexity is causing a …

In this work, we present a C ompiler-integrated, E xtensible D omain Specific System on
Chip R untime (CEDR) ecosystem to facilitate research toward addressing the challenges of …

Matrix factorization (MF) is used by many popular algorithms such as collaborative filtering.
GPU with massive cores and high memory bandwidth sheds light on accelerating MF much …

Massively multithreaded GPUs achieve high throughput by running thousands of threads in
parallel. To fully utilize the hardware, workloads spawn work to the GPU in bulk by launching …

From edge to exascale, computer architectures are becoming more heterogeneous and
complex. The systems typically have fat nodes, with multicore CPUs and multiple hardware …

Swift/T is a high-level language for writing concise, deterministic scripts that compose serial
or parallel codes implemented in lower-level programming models into large-scale parallel …

Accelerator-based systems are making rapid inroads into becoming platforms of choice for
high end cloud services. There is a need therefore, to move from the current model in which …

Agent-based models (ABMs) integrate the multiple scales of behavior and data to produce
higher order dynamic phenomena and are increasingly used in the study of important social …

Scientific applications with single instruction, multiple data (SIMD) computations show
considerable performance improvements when run on today's graphics processing units …