The exponential growth in the scale and complexity of neural recording systems demands increasingly efficient computational frameworks to manage the vast data generated by modern multi-electrode ar- rays. This thesis explores the feasibility of integrating Graphics Processing Uni
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The exponential growth in the scale and complexity of neural recording systems demands increasingly efficient computational frameworks to manage the vast data generated by modern multi-electrode ar- rays. This thesis explores the feasibility of integrating Graphics Processing Unit (GPU) acceleration with the Open Ephys ONIX system to enhance its support for next-generation neural interfaces and compu- tational algorithms. ONIX, a modular and open-source electrophysiology platform, currently relies on CPU-based computations that may struggle to meet the rising demands of closed-loop experiments. This work investigates PCIe peer-to-peer (P2P) communication between ONIX and GPUs to by- pass CPU involvement, aiming to reduce latency and increase throughput. A secondary data channel was implemented on the ONIX FPGA, accompanied by a custom kernel driver integrating GPUDirect with the existing RIFFA driver. Performance evaluations compared unpinned and pinned GPUDirect transfers to traditional CPU-mediated methods. Pinned transfers reduced transfer times by up to 30% for small data and 14% for larger data sizes, though significant variance in transfer time was observed. Application-level benchmarks revealed that GPU acceleration provides advantages for larger data sizes and higher computational loads but the effectiveness of GPUDirect is limited by ONIX’s PCIe through- put. The findings highlight that while GPUDirect offers theoretical benefits, its practical impact is con- strained by hardware limitations, including the PCIe bandwidth of the ONIX FPGA. This thesis out- lines necessary hardware improvements, such as higher-speed SerDes modules and advanced FPGA cards, to fully realize the potential of GPU acceleration in electrophysiology systems. A decision tree framework is proposed to guide researchers in determining scenarios where GPU and GPUDirect in- tegration is beneficial. While this study demonstrates the feasibility of GPU integration with ONIX, GPUDirect integration is currently not a beneficial technology for the ONIX ecosystem.
