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Wednesday April 24, 2019
Start: 24.04.2019 15:00

CAB G 51

 Moritz Hoffmann - PhD Defense: Managing and understanding distributed stream processing

Thursday April 25, 2019
Start: 25.04.2019 10:00

Thursday, 25. April 2019, 10:00-11:00 in CAB E 72

Speaker: Peter Pietzuch (Imperial College London)

Title: Scaling Deep Learning on Multi-GPU Servers





With the widespread availability of GPU servers, scalability in terms of the number of GPUs when training deep learning models becomes a paramount concern. For many deep learning models, there is a scalability challenge: to keep multiple GPUs fully utilised, the batch size must be sufficiently large, but a large batch size slows down model convergence due to the less frequent model updates.

In this talk, I describe CrossBow, a new single-server multi-GPU deep learning system that avoids the above trade-off. CrossBow trains multiple model replicas concurrently on each GPU, thereby avoiding under-utilisation of GPUs even when the preferred batch size is small. For this, CrossBow (i) decides on an appropriate number of model replicas per GPU and (ii) employs an efficient and scalable synchronisation scheme within and across GPUs.

Short Bio:

Peter Pietzuch is a Professor at Imperial College London, where he leads the Large-scale Data & Systems (LSDS) group ( in the Department of Computing. His research focuses on the design and engineering of scalable, reliable and secure large-scale software systems, with a particular interest in performance, data management and security issues. He has published papers in premier international venues, including SIGMOD, VLDB, OSDI, USENIX ATC, EuroSys, SoCC, ICDCS, CCS, CoNEXT, NSDI, and Middleware. Before joining Imperial College London, he was a post-doctoral fellow at Harvard University. He holds PhD and MA degrees from the University of Cambridge.

Friday May 17, 2019
Start: 17.05.2019 12:00

Friday, 17. May 2019, 12:00-13:00 in CAB E 72

Speaker: Tim Kraska (MIT)

Title: Towards Learned Algorithms, Data Structures, and Systems




All systems and applications are composed from basic data structures and algorithms, such as index structures, priority queues, and sorting algorithms. Most of these primitives have been around since the early beginnings of computer science (CS) and form the basis of every CS intro lecture. Yet, we might soon face an inflection point: recent results show that machine learning has the potential to alter the way those primitives or systems at large are implemented in order to provide optimal performance for specific applications. In this talk, I will provide an overview on how machine learning is changing the way we build systems and outline different ways to build learned algorithms and data structures to achieve “instance-optimality” with a particular focus on data management systems.

Short Bio:

Tim Kraska is an Associate Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory and co-director of the Data System and AI Lab at MIT (DSAIL@CSAIL). Currently, his research focuses on building systems for machine learning, and using machine learning for systems. Before joining MIT, Tim was an Assistant Professor at Brown, spent time at Google Brain, and was a PostDoc in the AMPLab at UC Berkeley after he got his PhD from ETH Zurich. Tim is a 2017 Alfred P. Sloan Research Fellow in computer science and received several awards including the 2018 VLDB Early Career Research Contribution Award, the 2017 VMware Systems Research Award


Monday May 20, 2019
Start: 20.05.2019 17:00

HG D22

 David Sidler - PhD Defense: In-Network Data Processing using FPGAs

Friday May 24, 2019
Friday May 31, 2019
Start: 31.05.2019 12:15

CAB E 72

Lunch Semiar Talk by Jansen Zhao

Title: A brief introduction to quantum computing with plausible applications to machine learning


I will give a briefly introduction to the main concepts in quantum information and quantum computing, and review the basic set of quantum algorithmic primitives. I will then show, by the example of Gaussian processes, how these quantum building blocks can be combined and provide computational speedup in machine learning. We will discuss the practical utility of these quantum algorithms and explore the domain of anticipated near-term applications of quantum computing.

Thursday July 11, 2019
Start: 11.07.2019 10:00

Thursday, 11. July 2019, 11:00-12:00 in CAB E 72

Speaker: Boris Grot (University of Edinburgh)

Title: Scale-Out ccNUMA: Embracing Skew in Distributed Key-Value Stores







Key-value stores (KVS’s) underpin many of today’s cloud services. For scalability and performance, state-of-the-art KVS systems distribute the dataset across a pool of servers, each of which holds a shard of data in memory and serves queries for the data in the shard. An important performance bottleneck that a KVS design must address is the load imbalance caused by skewed popularity distributions, whereby the “hot” items are accessed much more frequently than the rest of the dataset. Despite recent work on skew mitigation, existing approaches are limited in their efficacy when it comes to high-performance in-memory KVS deployments.

In this talk, I will discuss our recent work on skew mitigation for distributed in-memory KVS’s. We embrace popularity skew as a performance opportunity by aggressively caching popular items at all nodes of the KVS. The main challenges for such a design is maintaining the caches consistent while avoiding serialization points that can become a performance bottleneck at high load. I will describe our fully de-centralized caching architecture and the cache-coherence-inspired protocol used to keep the distributed caches consistent. I will also present simple protocol extensions that enable fault tolerance, with applicability beyond skew-tolerant KVS's.


Boris Grot is an Associate Professor in the School of Informatics at the University of Edinburgh. His research seeks to address efficiency bottlenecks and capability shortcomings of processing platforms for data-intensive applications. Boris is a member of the MICRO Hall of Fame and a recipient of various awards for his research, including IEEE Micro Top Pick and the Best Paper Award at HPCA 2019. Boris holds a PhD in Computer Science from The University of Texas at Austin and had spent two years as a post-doctoral researcher at EPFL.


Monday September 16, 2019
Thursday September 19, 2019
Start: 19.09.2019 10:00

Thursday, 19. September 2019, 10:00-11:00 in CAB E 72

 Speakers: Martin Hentschel/Max Heimel (Snowflake) 

Title: Micro Partitions at Snowflake: The Secret to Pruning, Zero-Copy Cloning, and Time Travel



Snowflake is an analytic data warehouse offered as a fully-managed service in the cloud. It is faster, easier to use, and far more scalable than traditional on-premise data warehouse offerings and is used by thousands of customers around the world. Snowflake's data warehouse is not built on an existing database or "big data" software platform such as Hadoop—it uses a new SQL database engine with a unique architecture designed for the cloud. This talk provides an overview of Snowflake’s architecture that was designed to efficiently support complex analytical workloads in the cloud. Looking at the lifecycle of micro partitions, this talk explains pruning, zero-copy cloning, and instant time travel. Pruning is a technique to speed up query processing by filtering out unnecessary micro partitions during query compilation. Zero-copy cloning allows to create logical copies of the data without duplicating physical storage. Instant time travel enables the user to query data "as of" a time in the past, even if the current state of the data has changed. This talk also shows how micro partitions tie into Snowflake's unique architecture of separation of storage and compute, and enable advanced features such as automatic clustering.

Speakers bio:

Martin Hentschel received a PhD in Computer Science from the Systems Group at ETH Zurich in 2012. In the following he worked at Microsoft where he built products integrating data from social networks into the Bing search engine. In 2014, he joined Snowflake where he is working on security, meta data management, and stateful micro services.

Max Heimel holds a PhD in Computer Science from the Database and Information Management Group at TU Berlin. He joined Snowflake in 2015 and is working primarily in the areas of query execution and query optimization. Before joining Snowflake, Max worked at IBM and spent several internships at Google.  


Sunday January 19, 2020
Monday January 20, 2020
Tuesday January 21, 2020
Wednesday January 22, 2020
Start: 19.01.2020
End: 22.01.2020