Electronic Visualization Laboratory

Power-constrained HPC systems increasingly run heterogeneous CPU–GPU applications under strict cluster-wide power limits. Existing cluster-wide power management policies rely on fair-share or utilization heuristics and do not capture application-specific sensitivity to CPU and GPU power caps, leading to inefficient use of reclaimed power. We present EcoShift, a performance-aware cluster-wide power management framework. EcoShift combines online performance prediction with a dynamic-programming-based allocator to distribute reclaimed power across CPU–GPU applications for maximum average performance improvement. Through emulation-based evaluation on two heterogeneous Intel CPU and NVIDIA A100/H100 GPU platforms with diverse CPU–GPU workloads, EcoShift consistently outperforms state-of-the-art policies, achieving up to 6% average performance improvement while preserving the cluster-wide power constraint.

Modern multi GPU HPC systems expose substantial computational capacity, yet inefficient GPU allocation often leads to wasted energy and underutilization. In practice, GPU applications exhibit heterogeneous and nonlinear scaling, making it inefficient to always use all available GPUs. We present EcoSched, an online scheduler that jointly optimizes GPU count selection and application coscheduling to improve workload level efficiency on multi GPU systems. EcoSched uses lightweight runtime profiling to estimate relative performance across GPU counts, applies a score based policy to balance energy efficiency and idle resources, and incorporates NUMA aware placement to mitigate interference. We implement EcoSched on heterogeneous CPU GPU platforms and evaluate it with diverse workloads on H100, A100, and V100 systems. EcoSched achieves up to 14.8% energy savings, 30.1% makespan improvement, and 40.4% EDP reduction over baseline schedulers, with modest performance overhead. These results show that jointly selecting GPU counts and coscheduling actions is essential for efficient multi GPU workload execution.

Symptom modelling in head and neck cancer is challenged by the complexity of heterogeneous patient data, leading to an interest in deep learning approaches. Although Long Short-Term Memory Networks (LSTMs) have shown great results in patient risk prediction, their low interpretability requires data modellers to collaborate with clinical experts to validate the results. We present L-VISP, a human–machine solution that uses visual analytics for LSTM modelling in clinical research. L-VISP uses custom visual encodings to make multiple LSTM variants interpretable, supporting a full range of analysis, from understanding model operations and evaluating performance to interpreting results in a clinical context. We evaluate L-VISP with data modellers and a clinical oncologist and present the takeaways from this multidisciplinary collaboration.

Recent advances in exascale computing have increased the resolution and fidelity of large-scale simulations, while rapid progress in deep learning has accelerated efforts to couple machine learning with physics-based solvers. We present a lightweight, modular in situ coupling methodology that embeds machine learning training and inference directly into simulation workflows using the ParaView and Catalyst APIs. The approach provides C++/Python interoperability via a solver-side data adaptor that packages simulation state into Conduit Nodes and a Catalyst-driven Python “bridge script” that converts solver fields into NumPy/PyTorch representations with minimal intrusion into the solver code. We describe the design and instrumentation required to integrate the framework and demonstrate it within a proxy (mini-app) of the HARVEY vascular flow solver. To illustrate practical usage, we implement both in situ training and in situ inference of a point-cloud autoencoder running concurrently with the solver. We report scalability and overhead characteristics and show that the approach enables distributed online ML workflows without language unification or major solver refactoring.