While MapReduce is inherently designed for batch and high throughput processing
workloads, there is an increasing demand for non-batch processes on big data, eg …

In recent years, with the trend of applying deep learning (DL) in high performance scientific
computing, the unique characteristics of emerging DL workloads in HPC raise great …

Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse kernel. Numerous
methods have been developed to accelerate SpMV. However, no single method consistently …

This paper presents vbench, a publicly available benchmark for cloud video services. We
are the first study, to the best of our knowledge, to characterize the emerging video-as-a …

Abstract Distributed Stream Processing Systems (DSPS) are “Fast Data” platforms that allow
streaming applications to be composed and executed with low latency on commodity …

Current trends in industrial systems opt for the use of different big-data engines as a means
to process huge amounts of data that cannot be processed with an ordinary infrastructure …

The complexity and diversity of big data and AI workloads make understanding them difficult
and challenging. This paper proposes a new approachto modelling and characterizing big …

Anomaly detection is an important aspect of data mining, where the main objective is to
identify anomalous or unusual data from a given dataset. However, there is no formal …

Memory bandwidth is a highly performance-critical shared resource on modern computer
systems. To prevent the contention on memory bandwidth among the collocated workloads …

The growth and enhancement of technology in the corporate society has led to data storage
and confidentiality issues. The problem arises from the management of trillions of data …