Computing platforms are evolving rapidly along many dimensions: processors, specialization, disaggregation, acceleration, smart memory and storage, etc. Many of these developments are being driven by data science but also arise from the need to make cloud computing more efficient. From a practical perspective, the result we see today is a deluge of possible configurations and deployment options, most of them too new to have a precise idea of their performance implications and lacking proper support in the form of tools and platforms that can manage the underlying diversity. The growing heterogeneity is opening up many opportunities but also raising significant challenges. In the talk I will describe the trend towards specialization at all layers of the architecture, the possibilities it opens up, and demonstrate with real examples how to take advantage of heterogeneous computing platforms. I will also discuss a system we are building for data processing considering heterogeneity both on the software as well as on the hardware side.

Gustavo Alonso is a professor in the Department of Computer Science of ETH Zurich where he is a member of the Systems Group (www.systems.ethz.ch) and the head of the Institute of Computing Platforms. He leads the AMD HACC (Heterogeneous Accelerated Compute Cluster) deployment at ETH (https://github.com/fpgasystems/hacc), with several hundred users worldwide, a research facility that supports exploring data center hardware-software co-design. His research interests include data management, cloud computing architecture, and building systems on modern hardware. Gustavo holds degrees in telecommunication from the Madrid Technical University and a MS and PhD in Computer Science from UC Santa Barbara. Previous to joining ETH, he was a research scientist at IBM Almaden in San Jose, California. Gustavo has received 4 Test-of-Time Awards for his research in databases, software runtimes, middleware, and mobile computing. He is an ACM Fellow, an IEEE Fellow, a Distinguished Alumnus of the Department of Computer Science of UC Santa Barbara, and has received the Lifetime Achievements Award from the European Chapter of ACM SIGOPS (EuroSys).

Deep learning training is an expensive process that extensively uses GPUs. However, not all model training saturates the resources of a single GPU. This problem gets exacerbated with each new GPU generation offering more hardware resources. In this talk, we will first investigate methods to share GPU resources across model training jobs by collocating these jobs on the same GPU to improve hardware utilization. Then, we will explore work sharing opportunities in the data pipelines of model training, furthering the benefits of collocated training.

Pınar Tözün is an Associate Professor at IT University of Copenhagen. Before ITU, she was a research staff member at IBM Almaden Research Center. Prior to joining IBM, she received her PhD from EPFL. Her thesis received ACM SIGMOD Jim Gray Doctoral Dissertation Award Honorable Mention in 2016. Her research focuses on resource-aware machine learning, performance characterization of data-intensive systems, and scalability and efficiency of data-intensive systems on modern hardware.