Speaker
Description
In this talk, we will describe our experience with the GPULab cluster in accelerating EEG signal processing and data analysis. GPULab is a large GPU computing resource of IMEC (Belgium) accessible for researchers through the SLICES-SC research infrastructure project. The system consists of a set of heterogeneous clusters, each with their own characteristics (GPU model, CPU speed, memory, bus speed, etc.), allowing users to select the most appropriate hardware. Jobs run isolated within GPU-enabled Docker containers with dedicated CPUs, GPUs and memory for maximum performance.
Our goal is to develop a GPU-accelerated EEG processing pipeline that significantly reduces execution time of large scale EEG data analysis tasks. The 16-GPU HGX-2 (V100 based) GPULab node is especially suited to our planned multi-GPU development effort. We are working on the implementation of high-performance, massively parallel algorithms for EEG preprocessing steps (filtering, artefact removal, baseline removal, averaging), power spectral density estimation, various transforms (Fourier, Wavelet, Hilbert, etc.) and signal decomposition methods (Independent Component Analysis, Empirical Mode Decomposition, etc.). We are studying advanced algorithmic and technical solutions (communication avoiding algorithms, mixed-precision computing, tensor cores, in-kernel multi-GPU communication, etc.) to achieve high levels of scalability even on extreme scale supercomputers.
In the talk, we will describe the GPULab infrastructure, its access modes to users, the containerised software environment, and our experience with the system. We will also describe results of our multi-GPU performance testing and the potential factors affecting application performance, as well as show the current status of our EEG algorithm development work.
The cloud-based GPULab infrastructure has great potential for accelerating data-intensive computational jobs, and gave us the opportunity to carry out preliminary multi-GPU algorithm development. However, research on the scalability of large-scale multi-GPU EEG data analysis algorithms will require future work on peta- and pre-exascale supercomputers.
