Computing and Information Systems - Theses
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ItemA generic framework for the simulation of biologically plausible spiking neural networks on graphics processors( 2011)The study of the structure and functionality of the brain has been ardently investigated, as the implications of such research may aid in the treatment and diagnosis of mental diseases. This has led to a growing interest in numerical simulation tools that can model its network complexity, in order to achieve a greater understanding of the underlying processes of this complex biological system. The computational requirements of neural modeling makes high performance multi-core systems a desirable architecture when simulating large-scale networks. Graphics processing units (GPUs) are an inexpensive, power-efficient, supercomputing alternative for solving compute-intensive scientific applications. However, the irregular communication and execution patterns in realistic spiking neural networks pose a challenge to their implementation on these massively data parallel devices. In this work, we propose a generic framework for simulating large-scale spiking neural networks with biologically realistic connectivity on GPUs. We provide an extensive list of optimization techniques and strategies which target the main issues involved with neural simulation on these devices, such as: optimal access patterns, synaptic referencing, current aggregation, firing representation, and task distribution. We succeed in building a GPU-based simulator that preserves the flexibility, accuracy, and biological plausibility of neural simulation, while providing high performance and efficient memory usage. Overall, our implementation achieves speedups of around 35-84 times on a single graphics card over an optimized CPU implementation based on the SPIKESIM simulator. We also provide a comparison with other GPU neural simulators related to this work. Following that, we analyze the communication aspects of migrating the system onto a multi-GPU cluster. This is done in an attempt to quantitatively determine the implications of communication overhead with large-scale neural simulation, when employing distributed clusters with GPU devices. We describe a model to determine the arising dependency cost from partitioning a neural network across the different components of the distributed system. We also discuss various techniques for minimizing overhead resulting from frequent messaging and global synchronization. Finally, we provide a theoretical analysis of the suggested communication model in relation to computational and overall performance, as well as a discussion on the relevance of the work.