GPUs and other accelerators are popular devices for accelerating compute-intensive,
parallelizable applications. However, programming these devices is a difficult task. Writing …

Processor manufacturers build increasingly specialized processors to mitigate the effects of
the power wall in order to deliver improved performance. Currently, database engines have …

Visual understanding of 3-D environments in real time, at low power, is a huge
computational challenge. Often referred to as simultaneous localization and mapping …

Kokkos is a programming model for writing performance portable applications for all major
high performance computing platforms. It provides abstractions for data management and …

The proliferation of heterogeneous hardware in recent years means that every system we
program is likely to include a mix of compute elements; each with different characteristics. By …

Real-time 3D space understanding is becoming prevalent across a wide range of
applications and hardware platforms. To meet the desired Quality of Service (QoS) …

Python is a popular language for end-user software development in many application
domains. End-users want to harness parallel compute resources effectively, by exploiting …

We present ALPyNA, an automatic loop parallelization framework for Python, which
analyzes data dependences within nested loops and dynamically generates CUDA kernels …

Heterogeneous computing has emerged as a means to achieve high performance and
energy efficiency. Naturally, this trend has been accompanied by changes in software …

GPUs are popular devices for accelerating scientific calculations. However, as GPU code is
usually written in low-level languages, it breaks the abstractions of high-level languages …