GPU-based Machine Learning System for fundamental biological research

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In the past decade, biological sciences have witnessed a major shift towards data-driven research. Consequently, high-performance computing has become a standard research tool in life sciences. Biological data, however, is not only large but it is highly complex. This complexity requires alternative approaches to conventional data analysis. Machine learning (ML) has emerged as a powerful methodology that can successfully tackle the analysis of complex biological data. It is a hopeless task to attempt to develop a mathematical model of an elephant. Yet, a three-year-old child can with ease point at an elephant in a photo. The child was shown a picture of an elephant and told that the object in the picture was an elephant. In other words, she learnt to recognise an elephant by seeing photos of it and now she can identify it on her own. ML emulates the learning process on a computer. Instead of building a precise description of patterns, the computer is "taught" to recognise them. This is a paradigm shift from conventional computing and, thus, has its own challenges. Notably, the brain is well suited for learning by example, yet it performs poorly when it comes to long divisions. Computers, on the other hand, have been designed to perform numerical operations with great speed and precision. It is, therefore, not surprising that emulating an inherently heuristic process such as learning on a computer would require a substantial computational effort. With the recent advent in Graphics Processing Unit (GPU) and Solid State Drive (SSD) technologies, the necessary computer power has become broadly available. It is, however, not surprising that traditional High-Performance Computing facilities are not well-suited for ML applications. ML has been successfully used in biology for more than two decades. An excellent example is the prediction of the viability of cancer cells when exposed to a drug. The idea is to associate a response (e.g., whether a cancer cell survives or not) to a set of characteristics or features (e.g., which genes were mutated and what chemical properties of the drug are). In the so-called supervised learning, the machine is presented with a large set of training data that contains correct responses for given input parameters. Based on that data, the machine learns to predict the response for new, previously unseen parameters. A major challenge is that it is often not easy to identify what the appropriate features are. Cells are very complex and it is often unclear which are the most relevant features that determine a specific response, e.g., mutations of which genes one should consider, etc. An expert is, therefore, required to prepare the appropriate training set. In recent years, so-called deep learning techniques have revolutionised the learning process by allowing the machine to automatically extract the key features from raw data. This is achieved by a set of model neurons, inspired by biological neural cells, organised in a layered network (i.e., a neural network). The information propagates through layers of the network, which enables each layer to capture more and more abstract features in the data. This drastically reduces the need for carefully tailored training sets and makes the ML applicable to a wider range of problems, especially those where expert-made training sets are not available or too costly to make. Deep learning ML approaches, however, require substantial computational resources. Typical deep learning neural networks contain tens to hundreds of layers, thousands of neurons, and hundreds of thousands of links between them. Training them, therefore, requires hardware that operates at TFLOPS speeds (trillions of operations per second) and can access the data at several GB/s.The aim of this proposal is to build a designated GPU-based system for applying deep-learning ML methods in fundamental biological research at the University of Dundee.