The last decade has witnessed an explosion in the amount of available biological sequence
data, due to the rapid progress of high-throughput sequencing projects. However, the …

This paper introduces wav2letter++, a fast open-source deep learning speech recognition
framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor library for …

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

Deep learning has had remarkable success in robotic perception, but its data-centric nature
suffers when it comes to generalizing to ever-changing environments. By contrast, physics …

In-memory databases require careful tuning and many engineering tricks to achieve good
performance. Such database performance engineering is hard: a plethora of data and …

As the computational requirements for machine learning systems and the size and
complexity of machine learning frameworks increases, essential framework innovation has …

A common operation in many data analytics workloads is to find the top-k items, ie, the
largest or smallest operations according to some sort order (implemented via LIMIT or …

We demonstrate a high-performance vendor-agnostic method for massively parallel solving
of ensembles of ordinary differential equations (ODEs) and stochastic differential equations …

GPUs are being widely used to accelerate different workloads and multi-GPU systems can
provide higher performance with multiple discrete GPUs interconnected together. However …

Training large neural networks on big datasets requires significant computational resources
and time. Transfer learning reduces training time by pre-training a base model on one …