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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.

• Session 6: Machine Learning for Optimisation of Computer Systems

• Seesion 7: Task scheduling, Performance, and Resource Optimisation

Large-Scale Data Processing and Optimisation, http://www.cl.cam.ac.uk/~ey204/teaching/ACS/R244_2017_2018/

1. Nando de Freitas, U of Oxford, Deep Learning, [Video] [Slides]

2. Oxford and DeepMind, Deep Natural Language Processing [Video] [Slides]

3. Advanced Topics in Deep Learning (Hung-yi Lee, NTU) [Video] [Slides]

4. Machine Learning (Hung-yi Lee, NTU) [Video] [Slides]

1. VMware Cloud Native Basics