Computing Platforms Seminar Series (COMPASS)

The Computing Platforms Seminar Series (COMPASS) is focused on talks by industry and academia around the general topic of computing platforms.

COMPASS is held on most Thursdays during the semester 10:00-11:00 (with some exceptions) in CAB E 72.

Upcoming Talks:

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

 Speakers: Martin Hentschel/Max Heimel (Snowflake) 

 Title: File Metadata Management at Snowflake





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.  

  Thursday, 26. September 2019, 10:00-11:00 in CAB E 72

 Speaker: Ben Zhao (University of Chicago).

 Title: Hidden Backdoors in Deep Learning Systems






Lack of transparency in today’s deep learning systems has paved the way for a new type of threats, commonly referred to as backdoor or Trojan attacks. In a backdoor attack, a malicious party can corrupt a deep learning model (either at initial training time or later) to embed hidden classification rules that do not interfere with normal classification, unless an unusual “trigger” is applied to the input, which would then produce unusual (and likely incorrect) results. For example, a facial recognition model with a backdoor might recognize anyone with a pink earring as Elon Musk. Backdoor attacks have been validated in a number of image classification applications, and are difficult to detect given the black-box nature of most DNN models.

In this talk, I will describe two recent results on detecting and understanding backdoor attacks on deep learning systems. I will first present Neural Cleanse (S&P 2019), the first robust tool to detect a wide range of backdoors in deep learning models. We use the idea of inter-label perturbation distances to detect when a backdoor trigger has created shortcuts to misclassification to a particular label. Second, I will describe our new work on Latent Backdoors (CCS 2019), a stronger type of backdoor attacks that are more difficult to detect, and survives retraining in commonly used transfer learning systems. We use experimental validation to show that latent backdoors can be quite robust and stealthy, even against the latest detection tools (including neural cleanse). There are no known techniques to detect latent backdoors, but we present alternative techniques to defend against them via disruption.


Ben Zhao is the Neubauer Professor of Computer Science at University of Chicago. He completed his PhD from Berkeley (2004) and his BS from Yale (1997). He is an ACM distinguished scientist, and recipient of the NSF CAREER award, MIT Technology Review's TR-35 Award (Young Innovators Under 35), ComputerWorld Magazine's Top 40 Tech Innovators award, Google Faculty award, and IEEE ITC Early Career Award. His work has been covered by media outlets such as Scientific American, New York Times, Boston Globe, LA Times, MIT Tech Review, and Slashdot. He has published more than 160 publications in areas of security and privacy, networked systems, wireless networks, data-mining and HCI (H-index > 60). He recently served as PC chair for World Wide Web Conference (WWW 2016) and the Internet Measurement Conference (IMC 2018), and is a general cochair for Hotnets 2020. 

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

Speaker: Norman May (SAP Research)

Title: Exploiting modern hardware in SAP HANA


SAP HANA has a long history of exploiting modern hardware to achieve high performance for database workloads. As a recent trend GPUs and FGPAs have the potential to offload work from general purpose CPUs or even accelerate operations previously executed on CPUs. In this talk I will share ongoing work in SAP HANA and potential application scenarios for accelerators along the query processing pipeline. I will also discuss current limitations for the usage of accelerators in productive SAP HANA scenarios.

Short Bio:

Norman May is a database architect and researcher of the HANA core database engine working on query processing and effective resource management. He supervises the research of several students in the SAP HANA campus and actively contributes to the database research community.

Thursday, 17. October 2019, 10:00-11:00 in CAB E 72

Speaker: Rene Müller (NVIDIA)

Title: TBA


Past COMPASS Talks:  

Date Speaker Affiliation Talk
11.07.2019 Boris Grot University of Edinburgh Scale-Out ccNUMA: Embracing Skew in Distributed Key-Value Stores
17.05.2019 Tim Kraska MIT Towards Learned Algorithms, Data Structures, and Systems
25.04.2019 Peter Pietzuch Imperial College London Scaling Deep Learning on Multi-GPU Servers
28.03.2019 Theo Rekatsinas
University of Wisconsin A Machine Learning Perspective on Managing Noisy Data
21.03.2019 Marko Vukolic IBM Research Hyperledger Fabric: a Distributed Operating System for Permissioned Blockchains
28.02.2019 Alberto Lerner University of Fribourg
The Case for Network-Accelerated Query Processing
21.02.2019 Thomas Würthinger Oracle Labs Bringing the Code to the Data with GraalVM
31.01.2019 Irene Zhang Microsoft Research, Redmond Demikernel: An Operating System Architecture for Hardware-Accelerated Datacenter Servers
25.10.2018 Mihnea Andrei SAP HANA Snapshot isolation in HANA - the evolution towards production-grade HTAP
04.10.2018 Philippe Bonnet IT University, Copenhagen, Denmark Near-Data Processing with Open-Channel SSDs
25.09.2018 Nandita Vijaykumar   Carnegie Mellon University Expressive Memory: Rethinking the Hardware-Software Contract with Rich Cross-Layer Abstractions
20.09.2018 Patrick Stüdi IBM Research Data processing at the speed of 100 Gbps using Apache Crail (Incubating)
15.08.2018 Leonid Yavits
Technion Resistive CAM based architectures: Resistive Associative In-Storage Processor and Resistive Address Decoder
06.07.2018 Martin Burtscher Texas State University Automatic Hierarchical Parallelization of Linear Recurrences
15.06.2018 Nitin Agrawal Samsung Research Low-Latency Analytics on Colossal Data Streams with SummaryStore
24.05.2018 Cagri Balkesen Oracle Labs RAPID: In-Memory Analytical Query Processing Engine with Extreme Performance per Watt
16.05.2018 Carsten Binnig TU Darmstadt Towards Interactive Data Exploration
09.05.2018 Bastian Hossbach Oracle Labs Modern programming languages and code generation in the Oracle Database
26.04.2018 Spyros Blanas Ohio State University Scaling database systems to high-performance computers
19.04.2018 Jane Hung MIT The Challenges and Promises of Large-Scale Biological Imaging
12.04.2018 Christoph Hagleitner IBM Research Heterogeneous Computing Systems for Datacenter and HPC Applications
14.03.2018  Eric Sedlar
 Oracle Labs
Why Systems Research Needs Social Science Added to the Computer Science
01.03.2018 Saughata Ghose Carnegie Mellon University How Safe Is Your Storage? A Look at the Reliability and Vulnerability of Modern Solid-State Drives
22.02.2018  Ioannis Koltsidas IBM Research Zurich System software for commodity solid-state storage