Hands are the primary means through which humans interact with the world. Reliable and always-available hand pose inference could yield new and intuitive control schemes for human-computer interactions, particularly in virtual and augmented reality. Computer vision is effective but requires one or multiple cameras and can struggle with occlusions, limited field of view, and poor lighting. Wearable wrist-based surface electromyography (sEMG) presents a promising alternative as an always-available modality sensing muscle activities that drive hand motion. However, sEMG signals are strongly dependent on user anatomy and sensor placement; existing sEMG models have thus required hundreds of users and device placements to effectively generalize for tasks other than pose inference. To facilitate progress on sEMG pose inference, we introduce the emg2pose benchmark, which is to our knowledge the first publicly available dataset of high-quality hand pose labels and wrist sEMG recordings. emg2pose contains 2kHz, 16 channel sEMG and pose labels from a 26-camera motion capture rig for 193 users, 370 hours, and 29 stages with diverse gestures - a scale comparable to vision-based hand pose datasets. We provide competitive baselines and challenging tasks evaluating real-world generalization scenarios: held-out users, sensor placements, and stages. This benchmark provides the machine learning community a platform for exploring complex generalization problems, holding potential to significantly enhance the development of sEMG-based human-computer interactions.

Summary Object storage technologies that take advantage of multitier storage on HPC systems are emerging. However, to use these technologies at present, applications have to be modified significantly from current I/O libraries. HDF5, a widely used I/O middleware on HPC systems, provides a virtual object layer (VOL) that allows applications to connect to different storage mechanisms transparently without requiring significant code modifications. We recently designed the proactive data containers (PDC) object‐centric storage system that provides the capabilities of transparent, asynchronous, and autonomous data movement taking advantage of multiple storage tiers—a decision that has so far been left upon the user on most current systems. To enable PDC's features through HDF5 without modifying application codes, we have developed an HDF5 VOL connector that interfaces with PDC. We present in this article the connector interface and evaluate its performance on Cori, a Cray XC40 supercomputer located at the National Energy Research Scientific Computing Center (NERSC). Our evaluation demonstrates up to an 8× improvement compared with HDF5 that has the most recent optimizations.

Emerging high performance computing (HPC) systems are expected to be deployed with an unprecedented level of complexity due to a deep system memory and storage hierarchy. Efficient and scalable methods of data management and movement through the multi-level storage hierarchy of upcoming HPC systems will be critical for scientific applications at exascale. In this paper, we propose in locus analysis that allows registering user-defined functions (UDFs) and running those functions automatically while the data is moving between levels of a storage hierarchy. We implement this analysis in the data path approach in our object-centric data management system, called Proactive Data Containers (PDC). The transparent invocation of analysis functions as part of PDC object mapping is an optimized approach to minimize latency to access data as it moves within the storage hierarchy. Because a user defined analysis or transform function will be invoked automatically by the PDC runtime, the user simply registers their functions for PDC to identify the function name as well as the required list of actual parameters. To demonstrate the validity and flexibility of this analysis approach, we have implemented several scientific analysis kernels to compare against other HPC analysis-oriented approaches.

Emerging high performance computing (HPC) systems are expected to be deployed with an unprecedented level of complexity due to a deep system memory and storage hierarchy. Efficient and scalable methods of data management and movement through this hierarchy is critical for scientific applications using exascale systems. Moving toward new paradigms for scalable I/O in the extreme-scale era, we introduce novel object-centric data abstractions and storage mechanisms that take advantage of the deep storage hierarchy, named Proactive Data Containers (PDC). In this paper, we formulate object-centric PDCs and their mappings in different levels of the storage hierarchy. PDC adopts a client-server architecture with a set of servers managing data movement across storage layers. To demonstrate the effectiveness of the proposed PDC system, we have measured performance of benchmarks and I/O kernels from scientific simulation and analysis applications using PDC programming interface, and compared the results with existing highly tuned I/O libraries. Using asynchronous I/O along with data and metadata optimizations, PDC demonstrates up to 23× speedup over HDF5 and PLFS in writing and reading data from a plasma physics simulation. PDC achieves comparable performance with HDF5 and PLFS in reading and writing data of a single timestep at small scale, and outperforms them at a scale of larger than 10K cores. In contrast to existing storage systems, PDC offers user-space data management with the flexibility to allocate the number of PDC servers depending on the workload.

On the road to exascale, the high-performance computing (HPC) community is seeing the emergence of multi-tier storage systems. However, existing data management solutions for HPC applications are no longer suitable for handling the increased level of storage complexity and currently delegate that task back to the user. We describe a novel object-based data abstraction that takes advantage of deep memory hierarchies by providing a simplified programming interface that enables autonomous, asynchronous, and transparent data movement with a server-driven architecture. Users can define a mapping between the application memory and abstract storage objects, creating a linkage between either all or part of an object's content without data copy or transfer, avoiding explicit management of complex data movement across multiple storage hierarchies. We evaluate our system by storing plasma physics simulation data with different storage layouts.