  The introduction of AI and ML technologies into medical devices has
revolutionized healthcare diagnostics and treatments. Medical device
manufacturers are keen to maximize the advantages afforded by AI and ML by
consolidating multiple applications onto a single platform. However, concurrent
execution of several AI applications, each with its own visualization
components, leads to unpredictable end-to-end latency, primarily due to GPU
resource contentions. To mitigate this, manufacturers typically deploy separate
workstations for distinct AI applications, thereby increasing financial,
energy, and maintenance costs. This paper addresses these challenges within the
context of NVIDIA's Holoscan platform, a real-time AI system for streaming
sensor data and images. We propose a system design optimized for heterogeneous
GPU workloads, encompassing both compute and graphics tasks. Our design
leverages CUDA MPS for spatial partitioning of compute workloads and isolates
compute and graphics processing onto separate GPUs. We demonstrate significant
performance improvements across various end-to-end latency determinism metrics
through empirical evaluation with real-world Holoscan medical device
applications. For instance, the proposed design reduces maximum latency by
21-30% and improves latency distribution flatness by 17-25% for up to five
concurrent endoscopy tool tracking AI applications, compared to a single-GPU
baseline. Against a default multi-GPU setup, our optimizations decrease maximum
latency by 35% for up to six concurrent applications by improving GPU
utilization by 42%. This paper provides clear design insights for AI
applications in the edge-computing domain including medical systems, where
performance predictability of concurrent and heterogeneous GPU workloads is a
critical requirement.
