Evaluation of different machine learning methods for MEG- based brain-function decoder

Application of machine learning methods for the analysis of functional neuroimaging signals, or 'brain-function decoding', is a highly interesting approach for better understanding of human brain functions. Recently, Kauppi et al. presented a brain-function decoder based on a novel feature extraction approach using spectral LDA, which allows both high classification accuracy (the authors used sparse logistic regression) and novel neuroscientific interpretation of the MEG signals. In this thesis we evaluate the performance of their brain-function decoder with additional classification and input feature scaling methods, providing possible additional options for their spectrospatial decoding toolbox SpeDeBox. We find the performance of their brain-function decoder to validate the potential of high frequency rhythmic neural activity analysis, and find that the logistic regression classifier provides the highest classification accuracy when compared to the other methods. We did not find additional benefits in applying prior input feature scaling or reduction methods

Parallelization of Variable Rate Decompression for GPU Acceleration

Data movement has been long identified as the biggest challenge facing modern computer systems designers. To tackle this challenge, many novel data compression algorithms have been developed. These compression algorithms can be embedded into bandwidth-bound applications to reduce their memory traffic volume. As a result, data decompression, in many instances, is in the critical path of the application execution, while the compression itself can happen offine or outside of the critical path. Therefore, fast data decompression is of utmost importance. However, most existing parallel decompression schemes adopt a particular parallelization strategy suited for a particular HW platform. Such an approach fails to harness the parallelism found in diverse modern HW architectures. To this end, we propose multiple parallelization strategies for variable rate data decompression. The proposed strategies aim to utilize parallel architectures efficiently. Our strategies are based on generating extra information during the encoding phase, and then passing this information in a side-channel to the decoder. After that, the decoder can use that extra information to speed-up the decoding process tremendously. To demonstrate the effectiveness of our strategies, we implement them in a state-of-the-art compression algorithm called ZFP and apply it on a real-life industrial application from ASML. Our implementation is publicly available on GitHub. This application is a feed-forward control model for controlling wafer heat in EUV lithography machines. The application is dominated by matrix-vector multiplication (which is bandwidth-bound) and is executed on GPUs. We show that parallelization strategies suited for multicore CPUs are different from the ones suited for GPUs. On a CPU, we achieve a near-optimal speedup and an overhead size which is consistently less than 0.04% of the compressed data size. On a GPU, we achieve a decoding throughput of more than 130 GiB/s which allows us to execute the ASML application within the given time budget

Voltametrische bepaling van stofoverdrachtscoefficiënten aan een draaiende en aan een trillende bol

Applied SciencesKramers Laboratorium voor Fysische Technologi

Parallelization of variable rate decompression through metadata

Data movement has long been identified as the biggest challenge facing modern computer systems' designers. To tackle this challenge, many novel data compression algorithms have been developed. Often variable rate compression algorithms are favored over fixed rate. However, variable rate decompression is difficult to parallelize. Most existing algorithms adopt a single parallelization strategy suited for a particular HW platform. Such an approach fails to harness the parallelism found in diverse modern HW architectures. We propose a parallelization method for tiled variable rate compression algorithms that consists of multiple strategies that can be applied interchangeably. This allows an algorithm to apply the strategy most suitable for a specific HW platform. Our strategies are based on generating metadata during encoding, which is used to parallelize the decoding process. To demonstrate the effectiveness of our strategies, we implement them in a state-of-the-art compression algorithm called ZFP. We show that the strategies suited for multicore CPUs are different from the ones suited for GPUs. On a CPU, we achieve a near optimal decoding speedup and an overhead size which is consistently less than 0.04% of the compressed data size. On a GPU, we achieve average decoding rates of up to 100 GiB/s. Our strategies allow the user to make a trade-off between decoding throughput and metadata size overhead.</p

Parallelization of variable rate decompression through metadata

Data movement has long been identified as the biggest challenge facing modern computer systems' designers. To tackle this challenge, many novel data compression algorithms have been developed. Often variable rate compression algorithms are favored over fixed rate. However, variable rate decompression is difficult to parallelize. Most existing algorithms adopt a single parallelization strategy suited for a particular HW platform. Such an approach fails to harness the parallelism found in diverse modern HW architectures. We propose a parallelization method for tiled variable rate compression algorithms that consists of multiple strategies that can be applied interchangeably. This allows an algorithm to apply the strategy most suitable for a specific HW platform. Our strategies are based on generating metadata during encoding, which is used to parallelize the decoding process. To demonstrate the effectiveness of our strategies, we implement them in a state-of-the-art compression algorithm called ZFP. We show that the strategies suited for multicore CPUs are different from the ones suited for GPUs. On a CPU, we achieve a near optimal decoding speedup and an overhead size which is consistently less than 0.04% of the compressed data size. On a GPU, we achieve average decoding rates of up to 100 GiB/s. Our strategies allow the user to make a trade-off between decoding throughput and metadata size overhead.Computer Engineerin