SPRT2: Scalable, Parallel, and Real-Time fMRI Data Analysis on Heterogeneous Architectures

Real-time functional Magnetic Resonance Imaging (fMRI) data analysis using the Sequential Probability Ratio Test (SPRT) enables dynamic adjustments to experimental protocols and early session termination, improving data quality and reducing patient fatigue. However, implementing SPRT in real-time fMRI analysis presents significant challenges due to the need for large-scale, high-dimensional data processing within strict time constraints. Furthermore, the ongoing advancements in fMRI hardware are driving a data explosion in the field, necessitating solutions that scale effectively. Existing approaches fall short in meeting real-time requirements and fail to fully exploit High-Performance Computing (HPC) and Big Data technologies. In this paper, we introduce Scalable, Parallel, and Real-Time Sequential Probability Ratio Test (SPRT ²), a toolkit that integrates HPC and Big Data techniques to enable efficient real-time SPRT-based fMRI data analysis. SPRT ² combines novel performance optimizations, such as hint-assisted matrix chain multiplication and sparse matrix techniques on heterogeneous architectures (CPUs and GPUs), with an Apache Spark-based framework for scalability and fault tolerance. Evaluated across 23 human subject experiments, SPRT ² achieves real-time analysis within the 1 -second repetition time while minimizing computational resource utilization (just 180 CPU cores). SPRT ² reduces session lengths by up to 33% and improves data quality. Furthermore, SPRT ² demonstrates near-linear scalability, efficiently processing synthetic datasets (35.9 billion voxels) over HPC platforms with 1,000 CPU cores or 8 NVIDIA A100 GPUs. To the best of our knowledge, SPRT ² is the first solution to integrate HPC and Big Data technologies for real-time fMRI analysis, setting a new standard in computational neuroscience. This work highlights the convergence of HPC and Big Data technologies and opens new avenues for tackling complex computational challenges in scalable and real-time fMRI data analysis.