A recurrent challenge in real‐world applications is autonomous management of the
executions at run‐time. In this vein, stream processing is a class of applications that compute …

On-device deep neural network (DNN) training holds the potential to enable a rich set of
privacy-aware and infrastructure-independent personalized mobile applications. However …

The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed
the design and development of various intelligent systems over wide application domains …

In a fast-changing data-driven world, real-time data processing systems are becoming
ubiquitous in everyday applications. The increasing data we produce, such as audio, video …

In stream processing, data arrives constantly and is often unpredictable. It can show large
fluctuations in arrival frequency, size, complexity, and other factors. These fluctuations can …

O crescimento e disseminação das arquiteturas paralelas têm conduzido a busca por maior
poder computacional com hardware massivamente paralelo tais como as unidades de …

GPUs are massively parallel processors that allow solving problems that are not viable to
traditional processors like CPUs. However, implementing applications for GPUs is …

Latency or throughput is often critical performance metrics in stream processing.
Applications' performance can fluctuate depending on the input stream. This unpredictability …

Abstract The evolution of Graphics Processing Units (GPUs) has allowed the industry to
overcome long-lasting problems and challenges. Many belong to the stream processing …

Nos dias de hoje, a maioria dos computadores são equipados com unidades de
processamento gráfico (GPUs) para prover capacidade massiva de paralelismo a baixo …