Sparse matrix-vector multiplication (SpMV) is a key operation across high-performance computing, graph analytics, and many more applications.In these applications, the matrix characteristics, notably non-zero elements per row, can vary widely and impact which algorithm performs best.Thus, Graphics Processing Unit (GPU) SpMV algorithms often rely on costly preprocessing to determine what per-row algorithm to select to achieve high performance.In this work we combine SpMV preprocessing and the subsequent per-row processing on the GPU by leveraging the novel "Work Graphs" GPU programming model-initially designed for graphics applications-for dynamic on-device self-scheduling.Work Graphs allow for fine-grain dataflow execution of individual workgroups using emerging hardware and firmware support.As soon as preprocessing has generated sufficient work, workgroups of individual processing kernels are self-scheduled and executed, interleaved with those of other kernels.This improves cache locality and eliminates host interaction altogether.Across a suite of 59 sparse matrices, the best of various novel Work Graphs SpMV implementations outperforms state-of-the-art rocSPARSE "LRB" for a single SpMV by up to 7.19× (mean: 3.35×, SD: 1.89).Furthermore, it achieves much more stable performance across various sparsity patterns than the rocSPARSE CSR-General algorithm, and even beats the advanced rocSPARSE CSR-Adaptive algorithm for up to 92 consecutive SpMV calculations.In addition, compared to rocSPARSE LRB, it reduces code complexity by 75%.Its memory footprint for supporting data structures is a fixed ∼25 MiB independent of matrix size, compared to rocSPARSE LRB's data structures that scale with matrix

As previous studies have shown, the environment of creative people can have a significant impact on their creative process and thus on their creations. However, with the advent of digital tools such as virtual instruments and digital audio workstations, more and more creative work is digital and decoupled from the creator’s environment. Virtual Reality technologies open up new possibilities here, as creative tools can seamlessly merge with any virtual environment the user finds himself in. This paper reports on the human-centered design process of a VR application that aims at supporting the user’s individual needs to support their creativity while composing percussive beats in virtual environments. For this purpose, we derived factors that influence creativity from literature and conducted focus group interviews in order to learn how virtual environments and 3DUI can be designed for creativity support. In a subsequent laboratory study, we let users interact with a virtual step sequencer UI in virtual environments that were either customizable or fixed/unchangeable. By analyzing post-test ratings from music experts, self-report questionnaires, and user behavior data, we examined the effects of such customizable virtual environments on user creativity, user experience, flow, and subjective creativity support scales. While we did not observe a significant impact of this independent variable on user creativity, user experience or flow, we found that users had specific individual needs regarding their virtual surroundings and strongly preferred customizable virtual environments, even though the fixed virtual environment was designed to be creatively stimulating. We also observed consistently high flow and user experience ratings, which promote human-centered design of VR-based creativity support tools in a musical context.

In this paper, we enhance our knowledge-based and constraint-based approach of robot programming with the concept of Semantic Mates. They describe intended mechanical connections between parts of an assembly. This allows deriving appropriate assembly poses from the type of connection and the geometric properties of the involved parts. The paper presents an ontology-based representation of Semantic Mates that is used to augment object models with additional information regarding their potential use in an assembly. Such semantically annotated object models can be used in our instruction framework to program a robot to perform assembly tasks through simple drag-and-drop operations in a graphical user interface. We conducted a user study with 21 participants in order to evaluate the efficiency and usability of the Semantic Mates concept based on a use-case from the domain of mechanical assembly. Across different experience levels in robotics, the participants achieved a significantly faster workflow and improved perceived usability compared to the manual specification of constraint-based assembly operations.