With the advent of immersive virtual reality (VR) head-mounted displays (HMD), we envision that immersive VR will revolutionize the personal fitness experience in our daily lives. Toward this vision, we present JARVIS, a virtual exercise assistant that is able to provide an immersive and interactive gym exercise experience to a user. JARVIS is enabled by the synergy between Internet of Things (IoT) and immersive VR. JARVIS employs miniature IoT sensing devices removably attachable to exercise machines to track a multitude of exercise information including exercise types, repetition counts, and progress within each repetition in real time. Based on the tracked exercise information, JARVIS shows the user the proper way of doing the exercise in the virtual exercise environment, thereby helping the user to better focus on the target muscle group. We have conducted both in-lab experiments and a pilot user study to evaluate the performance and effectiveness of JARVIS, respectively. Our in-lab experiments with fifteen participants show that JARVIS is able to segment exercise repetitions with an average accuracy of 97.96% and recognize exercise types with an average accuracy of 99.08%. Our pilot user study with ten participants shows statistically significant improvements in perceived enjoyment, competence, and usefulness with JARVIS compared to a traditional machine exercise setting (p < 0.05). Finally, our surface electromyography (sEMG) signal analysis conducted during the pilot user study shows statistically significant improvement in terms of muscle activation (p < 0.01), indicating the potential of JARVIS in providing an engaging and effective guidance for machine exercises.

Achieving high performance and performance portability for large-scale scientific applications is a major challenge on heterogeneous computing systems such as many-core CPUs and accelerators like GPUs. In this work, we implement a widely used block eigensolver, Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG), using two popular directive based programming models (OpenMP and OpenACC) for GPU-accelerated systems. Our work differs from existing work in that it adopts a holistic approach that optimizes the full solver performance rather than narrowing the problem into small kernels (e.g., SpMM, SpMV). Our LOPBCG GPU implementation achieves a 2.8x-4.3x speedup over an optimized CPU implementation when tested with four different input matrices. The evaluated configuration compared one Skylake CPU to one Skylake CPU and one NVIDIA V100 GPU. Our OpenMP and OpenACC LOBPCG GPU implementations gave nearly identical performance. We also consider how to create an efficient LOBPCG solver that can solve problems larger than GPU memory capacity. To this end, we create microbenchmarks representing the two dominant kernels (inner product and SpMM kernel) in LOBPCG and then evaluate performance when using two different programming approaches: tiling the kernels, and using Unified Memory with the original kernels. Our tiled SpMM implementation achieves a 2.9x and 48.2x speedup over the Unified Memory implementation on supercomputers with PCIe Gen3 and NVLink 2.0 CPU to GPU interconnects, respectively.

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