Exploration of GPU Cache Architectures Targeting Machine Learning Applications

The computation power from graphics processing units (GPUs) has become prevalent in many fields of computer engineering. Massively parallel workloads and large data set capabilities make GPUs an essential asset in tackling today\u27s computationally intensive problems. One field that benefited greatly with the introduction of GPUs is machine learning. Many applications of machine learning use algorithms that show a significant speedup on a GPU compared to other processors due to the massively parallel nature of the problem set. The existing cache architecture, however, may not be ideal for these applications. The goal of this thesis is to determine if a cache architecture for the GPU can be redesigned to better fit the needs of this increasingly popular field of computer engineering. This work uses a cycle accurate GPU simulator, Multi2Sim, to analyze NVIDIA GPU architectures. The architectures are based on the Kepler series, but the flexibility of the simulator allows for emulation of newer features. Changes have been made to source code to expand on the metrics recorded to further the understanding of the cache architecture. Two suites of benchmarks were used: one for general purpose algorithms and another for machine learning. Running the benchmarks with various cache configurations led to insight into the effects the cache architecture had on each of them. Analysis of the results shows that the cache architecture, while beneficial to the general purpose algorithms, does not need to be as complex for machine learning algorithms. A large contributor to the complexity is the cache coherence protocol used by GPUs. Due to the high spacial locality associated with machine learning problems, the overhead needed by implementing the coherence protocol has little benefit, and simplifying the architecture can lead to smaller, cheaper, and more efficient designs

Playing Smart - Artificial Intelligence in Computer Games

Abstract: With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games

Architecture and Design of Medical Processor Units for Medical Networks

This paper introduces analogical and deductive methodologies for the design medical processor units (MPUs). From the study of evolution of numerous earlier processors, we derive the basis for the architecture of MPUs. These specialized processors perform unique medical functions encoded as medical operational codes (mopcs). From a pragmatic perspective, MPUs function very close to CPUs. Both processors have unique operation codes that command the hardware to perform a distinct chain of subprocesses upon operands and generate a specific result unique to the opcode and the operand(s). In medical environments, MPU decodes the mopcs and executes a series of medical sub-processes and sends out secondary commands to the medical machine. Whereas operands in a typical computer system are numerical and logical entities, the operands in medical machine are objects such as such as patients, blood samples, tissues, operating rooms, medical staff, medical bills, patient payments, etc. We follow the functional overlap between the two processes and evolve the design of medical computer systems and networks.Comment: 17 page

Critters in the Classroom: A 3D Computer-Game-Like Tool for Teaching Programming to Computer Animation Students

The brewing crisis threatening computer science education is a well documented fact. To counter this and to increase enrolment and retention in computer science related degrees, it has been suggested to make programming "more fun" and to offer "multidisciplinary and cross-disciplinary programs" [Carter 2006]. The Computer Visualisation and Animation undergraduate degree at the National Centre for Computer Animation (Bournemouth University) is such a programme. Computer programming forms an integral part of the curriculum of this technical arts degree, and as educators we constantly face the challenge of having to encourage our students to engage with the subject. We intend to address this with our C-Sheep system, a reimagination of the "Karel the Robot" teaching tool [Pattis 1981], using modern 3D computer game graphics that today's students are familiar with. This provides a game-like setting for writing computer programs, using a task-specific set of instructions which allow users to take control of virtual entities acting within a micro world, effectively providing a graphical representation of the algorithms used. Whereas two decades ago, students would be intrigued by a 2D top-down representation of the micro world, the lack of the visual gimmickry found in modern computer games for representing the virtual world now makes it extremely difficult to maintain the interest of students from today's "Plug&Play generation". It is therefore especially important to aim for a 3D game-like representation which is "attractive and highly motivating to today's generation of media-conscious students" [Moskal et al. 2004]. Our system uses a modern, platform independent games engine, capable of presenting a visually rich virtual environment using a state of the art rendering engine of a type usually found in entertainment systems. Our aim is to entice students to spend more time programming, by providing them with an enjoyable experience. This paper provides a discussion of the 3D computer game technology employed in our system and presents examples of how this can be exploited to provide engaging exercises to create a rewarding learning experience for our students

A Data Science Course for Undergraduates: Thinking with Data

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.Comment: 21 pages total including supplementary material