17 June 2021

Abstract:
Large graphs are behind many problems in today’s computing landscape. The
growing sizes of such graphs, reaching 70 trillion edges recently, require
unprecedented amounts of compute power, storage, and energy. In this talk, we
illustrate how to effectively process such extreme-scale graphs. We will first
discuss Slim Graph, the first approach for practical lossy graph compression,
that facilitates high-performance approximate graph processing, storage, and
analytics with strong theoretical guarantees on accuracy, for a broad set of
graph problems and algorithms. In the second part of the talk, we will focus on
a class of complex graph mining problems such as clique listing. Here, we first
show how to solve such problems on complex parallel architectures in a simple
and high-performance way. For this, we propose a novel set-centric paradigm,
where complex algorithms are broken down into simple set algebra building
blocks such as set intersection or union, which can be separately optimized,
executed, and scheduled. Moreover, after discussing how to effectively and
efficiently mine complex graph patterns, we will turn our attention to pattern
prediction. Specifically, we establish a general problem of motif prediction,
in which we generalize link prediction, one of central problems in graph
analytics, into predicting the arrival of arbitrary complex higher-order
structures called motifs. To solve motif prediction, we harness recent graph
neural network architectures.

Bio
Maciej is a researcher from Scalable Parallel Computing Lab at ETH Zurich. He works on large-scale irregular computations and high-performance networking. He received Best Paper awards and Best Student Paper awards at ACM/IEEE Supercomputing 2013, 2014, and 2019, at ACM HPDC 2015 and 2016, ACM Research Highlights 2018, and several further best paper nominations (ACM HPDC 2014, ACM FPGA 2019, and ACM/IEEE Supercomputing 2019). He won, among others, the competition for the Best Student of Poland (2012), the first Google Fellowship in Parallel Computing (2013), and the ACM/IEEE-CS George Michael HPC Fellowship (2015). More detailed information on a personal site: https://people.inf.ethz.ch/bestam/

3 June 2021

Abstract: 
The emergence of big data in recent years due to the vast societal digitalization and large-scale sensor deployment has entailed significant interest in machine learning methods to enable automatic data analytics. In a majority of the learning algorithms used in industrial as well as academic settings, the first-order iterative optimization procedure Stochastic gradient descent (SGD), is the backbone. However, SGD is often time-consuming, as it typically requires several passes through the entire dataset to converge to a solution of sufficient quality. In order to cope with increasing data volumes, and to facilitate accelerated processing utilizing contemporary hardware, various parallel SGD variants have been proposed. In addition to traditional synchronous parallelization schemes, asynchronous ones have received particular interest in recent literature due to their improved ability to scale due to less coordination, and subsequently waiting time. However, asynchrony implies inherent challenges in understanding the execution of the algorithm and its convergence properties, due the presence of both stale and inconsistent views of the shared state. In this work, we aim to increase the understanding of the convergence properties of SGD for practical applications under asynchronous parallelism and develop tools and frameworks that facilitate improved convergence properties as well as further research and development. First, we focus on understanding the impact of staleness, and introduce models for capturing the dynamics of parallel execution of SGD. This enables (i) quantifying the statistical penalty on the convergence due to staleness and (ii) deriving an adaptation scheme, introducing a staleness-adaptive SGD variant MindTheStep-AsyncSGD, which provably reduces this penalty. Second, we aim at exploring the impact of synchronization mechanisms, in particular consistency-preserving ones, and the overall effect on the convergence properties. To this end, we propose Leashed-SGD, an extensible algorithmic framework supporting various synchronization mechanisms for different degrees of consistency, enabling in particular a lock-free and consistency-preserving implementation. In addition, the algorithmic construction of Leashed-SGD enables dynamic memory allocation, claiming memory only when necessary, which reduces the overall memory footprint. We perform an extensive empirical study, benchmarking the proposed methods, together with established baselines, focusing on the prominent application of Deep Learning for image classification on the benchmark datasets MNIST and CIFAR, showing significant improvements in converge time for Leashed-SGD and MindTheStep-AsyncSGD.

Bio:

Karl Bäckström is a Ph.D. student at the Distributed Computing and Systems group at Chalmers University of Technology in Sweden. Karl has an academic background in Mathematics, Computer Science, and Engineering physics, with an overarching interest in distributed and parallel computation, optimization, and machine learning. Karl’s research directions include adaptiveness, synchronization, and consistency in parallel algorithms for iterative optimization. At the 35th IEEE International Parallel and Distributed Processing Symposium, Karl with co-authors were awarded Best Paper Honorable Mention for the paper “Consistent Lock-free Parallel Stochastic Gradient Descent for Fast and Stable Convergence”. Karl lives in Gothenburg, a coastal city in western Sweden, together with his Swiss Shepherd Valdi, often enjoying their free time together in nature and wilderness, or at home playing the Piano.