Large-Scale Data Processing and Optimisation (2020-2021 Michaelmas Term)
This module provides an introduction to large-scale data processing, optimisation, and the impact on computer system's architecture. Large-scale distributed applications with high volume data processing such as training of machine learning will grow ever more in importance. Supporting the design and implementation of robust, secure, and heterogeneous large-scale distributed systems is essential. To deal with distributed systems with a large and complex parameter space, tuning and optimising computer systems is becoming an important and complex task, which also deals with the characteristics of input data and algorithms used in the applications. Algorithm designers are often unaware of the constraints imposed by systems and the best way to consider these when designing algorithms with massive volume of data. On the other hand, computer systems often miss advances in algorithm design that can be used to cut down processing time and scale up systems in terms of the size of the problem they can address. Integrating machine learning approaches (e.g. Bayesian Optimisation, Reinforcement Learning) for system optimisation will also be explored in this course. On completion of this module, the students should:
The module consists of 8 sessions, with 5 sessions on specific
aspects of large-scale data processing research. Each session discusses 3-4
papers, led by the assigned students. One session is a hands-on tutorial on
MapReduce using data flow programming and/or Deep Neural Networks using Google
TensorFlow. The first session advises on how to read/review a paper together
with a brief introduction on different perspectives in large-scale data
processing and optimisation. The last session is dedicated to the student
presentation of open-source project studies.
The module consists of 8 sessions, with 5 sessions on specific aspects of large-scale data processing research. Each session discusses 3-4 papers, led by the assigned students. One session is a hands-on tutorial on MapReduce using data flow programming and/or Deep Neural Networks using Google TensorFlow. The first session advises on how to read/review a paper together with a brief introduction on different perspectives in large-scale data processing and optimisation. The last session is dedicated to the student presentation of open-source project studies.
Schedule and Reading List
All the sessions will be online because of COVID-19 and we'll meet every Thursday (from October 8 to November 26) in 2020. The time slot is 10:00-12:00.1
2020/10/08 Session 1: Introduction to Large-Scale Data Processing and Optimisation
2020/10/15 Session 2: Data flow programming
1. Yuan Yu, Michael Isard, D. Fetterly, M. Budiu, U.
Erlingsson, P.K. Gunda, J. Currey:
2. M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma,
M. McCauley, M. Franklin, S. Shenker, I. Stoica:
*5. Derek Murray, Malte Schwarzkopf, Christopher Smowton,
Steven Smith, Anil Madhavapeddy and Steven Hand:
Frank McSherry's Talk on Differential Dataflow is
*6.3. F. McSherry, A. Lattuada, M. Schwarzkopf, T. Roscoe: Shared Arrangements: practical inter-query sharingfor streaming dataflows, VLDB, 2020.
7. P. Bhatotia, A. Wieder, R. Rodrigues, U. A. Acar, and R. Pasquini: Incoop: MapReduce for incremental computation, ACM SOCC, 2011.
*8.1 M. Abadi et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, Preliminary White Paper, 2015.
9. M. Looks et al.: Deep Learning with Dynamic Computation Graphs, ICLR, 2017.
10. M. Abadi, M. Isard and D. Murray: A Computational Model for TensorFlow - An Introduction, MAPL, 2017.
11.Y. Yu et al.: Dynamic Control Flow in Large-Scale Machine Learning, EuroSys, 2017.
14. T. Akidau, R. Bradshaw, C. Chambers, S. Chernyak, R. Fernandez-Moctezuma, R. Lax, S. McVeety, D. Mills, F. Perry, E. Schmidt, S. Whittle: The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing, VLDB, 2015.
15. S. Li, Y. Zhao, R. Varma, et. al: PyTorch Distributed: Experiences on Accelerating Data Parallel Training, VLDB, 2020.
2020/10/22 Session 3: Large-scale graph data processing
*1. G. Malewicz, M. Austern, A. Bik, J. Dehnert, I. Horn, N. Leiser, and G. Czajkowski: Pregel: A System for Large-Scale Graph Processing, SIGMOD, 2010.
2. Z. Qian, X. Chen, N. Kang, M. Chen, Y. Yu, T. Moscibroda, Z.Zhang: MadLINQ: large-scale distributed matrix computation for the cloud, EuroSys, 2012.
3. Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, J. Hellerstein: Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud, VLDB, 2012.
(slides)(Armins' blog on Ligra)
6. J. Gonzalez, R. Xin, A. Dave, D. Crankshaw, M. Franklin, I. Stoica: GraphX: Graph Processing in a Distributed Dataflow Framework, OSDI, 2014.
7. B. Shao, H. Wang, Y. Li: Trinity: A Distributed Graph Engine on a Memory Cloud, SIGMOD, 2013.
8. A. Kyrola and G. Blelloch: Graphchi: Large-scale graph computation on just a PC, OSDI, 2012.
10. A. Roy, L. Bindschaedler, J. Malicevic and W. Zwaenepoel: Chaos: Scale-out Graph Processing from Secondary Storage , SOSP, 2015.
11. F. McSherry, M. Isard and D. Murray: Scalability! But at what COST? , HOTOS, 2015.
12. X. Hu, Y. Tao, C. Chung: Massive Graph Triangulation, SIGMOD, 2013.
13. W. Xie, G. Wang, D.Bindel, A. Demers, J. Gehrke: Fast Iterative Graph Computation with Block Updates, VLDB, 2014.
14. S. Hong, H. Chafi, E. Sedlar, K.Olukotun: Green-Marl: A DSL for Easy and Efficient Graph Analysis, ASPLOS, 2012.
15. D. Prountzos, R. Manevich, K. Pingali: Elixir: A System for Synthesizing Concurrent Graph Programs, OOPSLA, 2012.
16. D. Nguyen, A. Lenharth, K. Pingali: A Lightweight Infrastructure for Graph Analytics, SOSP 2013.
17. D. Merrill, M. Garland, A. Grimshaw: Scalable GPU Graph Traversal, PPoPP, 2012.
18. A. Gharaibeh, E. Santos-Neto, L. Costa, M. Ripeanu: Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems, IEEE TPC, 2014.
20. H. Dai, Z. Kozareva, B. Dai, A. Smola and L. Song: Learning Steady-States of Iterative Algorithms over Graphs, ICML, 2018.
21. K. Nilakant, V. Dalibard,
A. Roy, and E. Yoneki:
PrefEdge: SSD Prefetcher for Large-Scale Graph Traversal.
ACM International Systems and Storage Conference (SYSTOR),
21. K. Nilakant, V. Dalibard, A. Roy, and E. Yoneki: PrefEdge: SSD Prefetcher for Large-Scale Graph Traversal. ACM International Systems and Storage Conference (SYSTOR), 2014.
22. L. Bindschaedler, J.
Malicevic, N. Schiper, A. Goel, W. Zwaenepoel:
Rock you like a Hurricane: taming skew in large scale
anaylitcs. EuroSys, 2018.
22. L. Bindschaedler, J. Malicevic, N. Schiper, A. Goel, W. Zwaenepoel: Rock you like a Hurricane: taming skew in large scale anaylitcs. EuroSys, 2018.
2020/10/29 Session 4: Map/Reduce and Deep Neural Network using TensorFlow Hands-on Tutorial
2020/11/05 Session 5: Many Aspects of Optimisation in Computer Systems
1. J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. Le, M. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, A. Ng.: Large scale distributed deep networks. NIPS, 2012.
2. G. Venkates et al.: Accelerating Deep Convolutional Networks using low-precision and sparsity, ICASSP, 2017.
3. V. Mnih et al.: Asynchronous Methods for Deep Reinforcement Learning, ICML, 2016.
4. J. Ansel et al. Opentuner: an extensible framework for program autotuning. PACT, 2014.
5. B. Bodin, L. Nardi, MZ Zia et al.: Integrating Algorithmic Parameters into Benchmarking and Design Space Exploration in 3D Scene Understanding, PACT, 2016.
*6. J. Ansel et al.: Petabricks: A language and compiler for algorithmic choice. In Proceedings of the 2009 ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI, 2009.
7. V. Mnih et al.: Playing Atari with Deep Reinforcement Learning, NIPS, 2013.
8. J. Snoek, H. Larochelle, and R. Adams: Practical Bayesian Optimization of Machine Learning Algorithms, NIPS, 2012.
9. B. Teabe et al.: Application-specific quantum for multi-core platform scheduler, EuroSys, 2016.
10. G. Tesauro et al.: A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation, ICAC, 2006.
*12. A. Klein, S. Falkner, S. Bartels,
P. Hennig, F. Hutter:
Optimization of Machine Learning Hyperparameters on Large Datasets,
*12. A. Klein, S. Falkner, S. Bartels, P. Hennig, F. Hutter: Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets, AISTAS, 2017.
13. T. Domhan, J. T. Springenberg, F.
Hutter: Speeding up automatic
hyperparameter optimization of deep neural networks by extrapolation of learning
curves, IJCAI, 2015.
13. T. Domhan, J. T. Springenberg, F. Hutter: Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves, IJCAI, 2015.
14. F. Hutter et al.: Algorithm runtime prediction: Methods&evaluation, Elsevier J. AI, 2014.
15. Z. Jia, M. Zaharia, and A. Aiken: Beyond Data and Model Parallelism for Deep Neural Networks, SYSML, 2019.
16. Ł. Kaiser et al.: Model Based Reinforcement Learning for Atari, arXiv, 2019.
17. H. Liu, K. Simonyan, and Y. Yang: DARTS: Differentiable Architecture Search, arXiv, 2018.
19. S. Palkar, J. Thomas, A.
Shanbhagy, D. Narayanan, H. Pirky, M. Schwarzkopfy, S. Amarasinghey, and M.
20. S. Palkar, J. Thomas, D. Narayanan, P. Thaker, R. Palamuttam, P. Negi, A. Shanbhag, M. Schwarzkopf, H. Pirk, S. Amarasinghe, S. Madden, M. Zaharia: Evaluating End-to-End Optimization for Data Analytics Applications in Weld, VLDB, 2018.
21. H. Dai, E. Khalil, Y. Zhang,
B. Dilkina, L. Song: Learning
Combinatorial Optimization Algorithms over Graphs, NIPS, 2017.
21. H. Dai, E. Khalil, Y. Zhang, B. Dilkina, L. Song: Learning Combinatorial Optimization Algorithms over Graphs, NIPS, 2017.
22. E. Liang et al.:
RLlib: Abstractions for
Distributed Reinforcement Learning, ICML, 2018.
22. E. Liang et al.: RLlib: Abstractions for Distributed Reinforcement Learning, ICML, 2018.
23. R. Liaw, E. Liang, R. Nishihara, P. Moritz, J. Gonzalez, I. Stoica: Tune: A Research Platform for Distributed Model Selection and Training, ICML, 2018.
24. D. Kingma, J. Ba:
A Method for Stochastic Optimization,
24. D. Kingma, J. Ba: Adam: A Method for Stochastic Optimization, ICLR, 2015.
25. Z. Jia, S. Lin, R. Ying, J. You,
J. Leskovec, A. Aiken: Redundancy-Free
Computation Graphs for Graph Neural Networks :
25. Z. Jia, S. Lin, R. Ying, J. You, J. Leskovec, A. Aiken: Redundancy-Free Computation Graphs for Graph Neural Networks
: ArXiv, 2019.
26. T. Chen et al.: Learning to Optimize
Tensor Programs, NIPS, 2018.
26. T. Chen et al.:
Learning to Optimize Tensor Programs, NIPS, 2018.
*27. T. Chen, T. Moreau, Z. Jiang, L. Zheng, S. Jiao, E. Yan, H. Shen, M. Cowan, L. Wang, Y. Hu, L. Ceze, C. Guestrin, and A. Krishnamurthy: TVM: An Automated End-to-End Optimizing Compiler for Deep Learning, OSDI, 2018.
*28. T. Chen, T. Moreau, Z. Jiang, L. Zheng, S. Jiao, E. Yan, H. Shen, M. Cowan, L. Wang, Y. Hu, L. Ceze, C. Guestrin, and A. Krishnamurthy: TVM: End-to-End Compilation Stack for Deep Learning, SysML, 2017.
29. H. Zhang et al.: Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters, ATC, 2017.
32. N. K. Ahmed, et al.: On Sampling from Massive Graph Streams, VLDB, 2017.
*33. Kraska, T., Alizadeh, M., Beutel, A., Chi, E.H., Ding, J., Kristo, A., Leclerc, G., Madden, S., Mao, H. and Nathan, V.: SageDB: A learned database system, CIDR, 2019.
34. Ma, L., Ding, B., Das, S. and Swaminathan, A.: Active Learning for ML Enhanced Database Systems, SIGMOD, 2020.
35. A. Kipf, R. Marcus, A. van Renen, M. Stoian, A. Kemper, T. Kraska, and T. Neumann: SOSD: A Benchmark for Learned Indexes, NeurIPS Workshop on ML for Systems, 2019.
36. D. Ha and J. Schmidhuber: World Models, arXiv, 2018 (https://worldmodels.github.io).
*37. L. Li te al.: A System for Massively Parallel Hyperparameter Tuning, MLSys, 2020.
2020/11/12 Session 6: Probabilistic Programming
Guest lecture: Brooks Paige (UCL, ATI)
Title: Programs as probabilistic models
Abstract: Probabilistic models used in quantitative sciences have historically co-evolved with methods for performing inference: specific modeling assumptions are made not because they are appropriate to the application domain, but because they are required to leverage existing software packages or inference methods. The emerging field of probabilistic programming aims to reduce the technical and cognitive overhead for writing and designing novel probabilistic models, by introducing a specialized programming language as an abstraction barrier between modeling and inference. While we would ideally be able to provide “automatic” inference for any probabilistic model, this proves severely challenging for models written in sufficiently expressive languages. In this talk I will discuss some of these difficulties, and provide an introduction and overview of different approaches to probabilistic programming.
Bio: Brooks Paige is an associate professor in machine learning at the University College London AI Centre. He is also a Turing fellow at the Alan Turing Institute, and a statistical ambassador for the Royal Statistical Society. He holds a D.Phil in Engineering Science from the University of Oxford, where he was supervised by Frank Wood; an M.A. in Statistics from Columbia University; and a B.A. in Mathematics from Amherst College.
Reading Club online @10:00
1. E. Bingham et al.: Pyro: Deep Universal Probabilistic Programming, Journal of Machine Learning Research, 2019.
2. D. Tran et al.: Edward: A library for probabilistic modeling, inference, and criticism, arXiv, 2017.
3. N. Goodman, V. Mansinghka, D. Roy, K. onawitz, J. Tenenbaum: Church: a language for generative models. In Proceedings of the Conference on Uncertainty in Arti cial Intelligence, UAI, 2008.
4. F. Wood, J. van de Meent, V. Mansinghka: A new approach to probabilistic programming inference, AISTATS, 2014.
5. B. Paige and F. Wood: A compilation target for probabilistic programming languages, ICML, 2014.
*6. J. Ai et al.: HackPPL: a universal probabilistic programming language, MAPL, 2019.
*8. Ge, H., Xu, K. and Ghahramani, Z.: Turing: A language for flexible probabilistic inference, AISTATS, 2018.
10. T. Rainforth et al.: Bayesian Optimization for Probabilistic Programs, NIPS, 2016.
*11. M. Balandat et al.: BOTORCH: Bayesian Optimization in PyTorch, Arxiv 2020.
2020/11/19 Session 7: Optimisation of Computer Systems using ML
*1.2. A. Mirhoseini, A. Goldie, H. Pham, B. Steiner, Q. Le and J. Dean: A Hierarchical Mode for Device Placement, ICLR, 2018.
*2. A. Mirhoseini and A. Goldie: Chip Placement with Deep Reinforcement Learning, ISPD, 2020.
*3. R. Addanki, S. B. Venkatakrishnan, S. Gupta, H. Mao, M. Alizadeh : Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning , arXiv, 2019.
4. F. Yang et al.: LFTF: A Framework for Efficient Tensor Analytics at Scale, VLDB, 2017.
*5. , SYSML, 2019.
, SYSML, 2019.
7. I. Gog, M. Schwarzkopf, A. Gleave, R.
Watson, S. Hand: Firmament: fast, centralized cluster scheduling at scale, OSDI,
9. C. Delimitrou et al.: Quasar: Resource-Efficient and QoS-Aware Cluster Management, ASPLOS, 2014.
10. H. Mao et al.: Neural Adaptive Video Streaming with Pensieve, SIGCOMM, 2017.
11. S. Venkataraman et al.: Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics, NSDI, 2016.
12. K. LaCurts et al.: Cicada: Introducing Predictive Guarantees for Cloud Networks, HOTCLOUD, 2014.
13. H. Hoffmann et al.: Dynamic Knobs for Responsive Power-Aware Computing, Asplos, 2011.
14. N.J. Yadwadkar, B. Hariharan, J. Gonzalez and R. Katz: Faster Jobs in Distributed Data Processing using Multi-Task Learning, SDM, 2015.
15. X. Dutreih et al.: Using Reinforcement Learning for Autonomic Resource Allocation in Clouds: Towards a Fully Automated Workflow, ICAS, 2011.
16. J. Eastep et al.: Smart Data Structures: An Online Machine Learning Approach to Multicore Data Structures, ICAC, 2011.
17. H. Mao, M. Schwarzkopf, S. B. Venkatakrishnan, Z. Meng, M. Alizadeh: Learning Scheduling Algorithms for Data Processing Clusters, SIGCOMM, 2019.
18. E. Ipek et al.: Self-Optimizing Memory Controllers: A Reinforcement Learning Approach, ISCA, 2008.
19. S. Teerapittayanon et al.: Distributed Deep Neural Networks over the Cloud, the Edge and End Devices, ICDCS, 2017.
20. B. Zoph et al.: Learning Transferable Architectures for Scalable Image Recognition, arXiv, 2017.
21. D. Golovin et al.: Google Vizier: A Service for Black-Box Optimization, KDD, 2017.
22. D. Baylor et al.: TFX: A TensorFlow-Based Production-Scale Machine Learning Platform, KDD, 2017.
23. H. Mao et al.: Resource Management with Deep Reinforcement Learning, HotNets, 2016.
24. M. Raghu et al.: On the Expressive Power of Deep Neural Networks, PMLR, 2017.
25. D. Aken et al.: Automatic Database Management System Tuning Through Large-scale Machine Learning, SIGMOD, 2017.
26. A. Pavlo et al.:
Management Systems, CIDR, 2017.
28. L. Espeholt et al.: IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures, ICML, 2018.
29. M. Carvalho et al.: Long-term SLOs for reclaimed cloud computing resources, SOCC, 2014.
*30. A. Ratner, S. Bach, H. Ehrenberg, J. Fries, S. Wu, and C. Ré: Snorkel: Rapid Training Data Creation with Weak Supervision, VLDB, 2017.
*31. A. Ratner, B. Hancock, J. Dunnmon, R. Goldman, and C. Ré: Snorkel MeTaL: Weak Supervision for Multi-Task Learning, DEEM, 2018.
32. A. Koliousis, P. Watcharapichat, M. Weidlich, L. Mai, P. Costa, P. Pietzuch: CROSSBOW: Scaling Deep Learning with Small Batch Sizes on MultiGPU Servers, VLDB, 2019.
33. A. Floratou et al.: Dhalion: self-regulating stream processing in Heron, VLDB, 2017.
34. T. Li, Z. Xu, J. Tang and Y. Wang: Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning, VLDB, 2018.
35. E. Lambart et al.: Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning, IEEE Robotics and Automation Letters, 2019.
36. Y. Kang et al.: Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge, ASPLOS, 2017.
37. Y. You et al.: Scaling Deep Learning on GPU and Knights Landing clusters, SC, 2017.
40. K. Tzoumas, A. Deshpande, and C. S. Jensen: Efficiently adapting graphical models for selectivity estimation, VLDB, 2013.
2020/11/26 Session 8: Presentation of Open Source Project Study
Coursework 1 (Reading Club)
The reading club will require you to read between 1
and 3 papers every week. You need to fill out simple review_log
(MS word format,
format) prior to each session and email me by Wednesday 12:00 (noon). The minimum requirement of review_log is one per session, but
you can read as many as you want and fill the review_log for each paper you
read. review_log is not marked but 'tick'.
Coursework 2 (Reports)
The following three reports are required, which could be extended from the reading assignment of the reading club or a different one within the scope of data centric networking.
The report 1 should be handed in by November 13, 2020 - 16:00 and the report 2 by December 4, 2020 - 16:00 . The report 3 should be by the end of the Michaelmas term (January 20, 2021 - 16:00 - but if you could finish by 21st of December, 2020 that will be good!).
The final grade for the course will be provided as a letter grade or percentage and the assessment will consist of two parts:
Open Source Projects
See the candidates of Open Source Projects in data centric networking. The list is not exhausted. If you take anything other than the one in the list, please discuss with me. The purpose of this assignment is to understand the prototype of the proposed architecture, algorithms, and systems through running an actual prototype and present/explain to the other people how the prototype runs, any additional work you have done including your own applications and setup process of the prototype. This experience will give you better understanding of the project. These Open Source Projects come with a set of published papers and you should be able to examine your interests in the paper through running the prototype. Some projects are rather large and may require extensive environment and time; make sure you are able to complete this assignment.
How to Read/Review a Paper
The following papers aid how to read/review a paper.
Further supplement: see ‘how to read/review a paper’ section in Advanced Topics in Computer Systems by Steven Hand
Presentations should be about 15-20 minutes long, where you need to cover the following aspects.
The following document aids in presenting a review.
How to write a survey paper
A survey paper provides the readers with an exposition of existing work that is comprehensive and organized. It must expose relevant details associated in the surveying area, but it is important to keep a consistent level of details and to avoid simply listing the different works. Thus a good survey paper should demonstrate a summary of recent research results in a novel way that integrates and adds understanding to work in the field. For example, you can take an approach by classifying the existing literature in your own way; develop a perspective on the area, and evaluate trends. Thus, after defining the scope of your survey, 1) classify and organize the trend, 2) critical evaluation of approaches (pros/cons), and 3) add your analysis or explanation (e.g. table, figure). Also adding reference and pointer to further in-depth information is important (summary from Rich Wolski’s note).
Papers for OS Principles (Distributed Storage and Deterministic Parallelism)
3. F. Chang et al: BigTable: A Distributed Storage System for Structured Data, USENIX OSDI 2006.
4. G. DeCandia et al: Dynamo: Amazon's Highly Available Key-value Store, ACM SOSP 2007.
6. A. Aviram, et al: Efficient System-Enforced Determistic Parallelism, USENIX OSDI 2010.
7. T. Liu et al: Dthreads: Efficient and Determistic Multithreading, ACM SOSP 2011.
Please email to email@example.com for the question.