Abstract Objective There are several meta-analyses of randomized controlled trials (RCTs) demonstrating the benefits of virtual reality (VR) training as an intervention for motor performance, activity of daily living (ADL) and quality of life (QoL) outcomes in patients with Parkinson's disease (PD). However, the aggregate evidence collected to date has not been thoroughly evaluated for strength, quality, and reproducibility. An umbrella review from published meta-analyses of RCTs was conducted to evaluate the strength and quality of existing evidence regarding the efficacy of VR training in improving the motor performance, ADL and QoL outcomes of patients with PD. Methods PubMed, PsychInfo, Web of Science, and Scopus were searched to identify relevant meta-analysis of RCTs examining the effects of VR training on motor performance and quality of life outcomes in PD patients. We recalculated the effect sizes (Hedges’g) for VR training using DerSimonian and Laird (DL) random effects models. We further assessed between-study heterogeneity, prediction interval (PI), publication bias, small-size studies, and whether the results of the observed positive studies were better than would be expected by chance. Based on these calculations, the quality of evidence for each outcome was assessed by using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria. Results Four meta-analysis with eight outcomes included in the umbrella review was recalculated effect size. Pooled results found VR training can large improve the basic balance ability, moderate improve the overall balance capacity and moderate improve the stride length in PD patients. For ADL and QoL, the effect sizes were pooled that suggested VR training can moderate improve ADL and QoL for PD patients. However, no statistically clear evidence was found in walking speed, motor function and gait function during VR training. The analyzed meta-analyses showed low-to-moderate methodological quality (AMSTAR2) as well as presented evidence of moderate-to-very low quality (GRADE). Tow adverse reactions were reported in the included meta-analyses. Conclusions In this umbrella review, a beneficial correlation between VR and balance ability, stride length, ADL and QoL in PD patients was discovered, especially for the very positive effect of VR on balance because of two of the eight outcomes related to balance ability showed large effect size. The observations were accompanied by moderate- to very low-quality rating evidence, supporting VR training as a practical approach to rehabilitation. 

Efficacy of virtual reality training on motor performance, activity of daily living, and quality of life in patients with Parkinson's disease: an umbrella review comprising meta-analyses of randomized controlled trials

Modern scientific applications utilize numerous software and hardware layers to efficiently access data. This approach poses a challenge for I/O optimization because of the need to instrument and correlate information across those layers. The Darshan characterization tool seeks to address this challenge by providing efficient, transparent, and compact runtime instrumentation of many common I/O interfaces. It also includes command-line tools to generate actionable insights and summary reports. However, the extreme diversity of today’s scientific applications means that not all applications are well served by one-size-fits-all analysis tools. In this work we present PyDarshan, a Python-based library that enables agile analysis of I/O performance data. PyDarshan caters to both novice and advanced users by offering ready-to-use HTML reports as well as a rich collection of APIs to facilitate custom analyses. We present the design of PyDarshan and demonstrate its effectiveness in four diverse real-world analysis use cases.

Enabling Agile Analysis of I/O Performance Data with PyDarshan

Characterization of Large-scale HPC Workloads with non-naïve I/O Roofline Modeling and Scoring

Performance tools are inseparable from complex HPC applications’ performance analysis and engineering life cycles. Due to the application’s complexity, various performance analysis tools are created to serve different analysis purposes and provide a deeper look at certain aspects of the applications. Although these tools might operate differently, having coherent information and consistent metrics across all tools is mandatory for ensuring analysis continuity. It is common for performance analysts to switch their usual performance tools due to various reasons and limitations. In this work, we look specifically at the I/O performance analysis tools landscape and introduce Mango-IO to verify the result consistencies between tools and provide tool-agnostic metrics calculation methods. Our analysis and case study provides lesson learned and guideline for ensuring measurement continuity and comparability.

Mango-IO: I/O Metrics Consistency Analysis

Manually diagnosing the I/O performance bottleneck for a single application (hereinafter referred to as the “job level”) is a tedious and error-prone procedure requiring domain scientists to have deep knowledge of complex storage systems. However, existing automatic methods for I/O performance bottleneck diagnosis have one major issue: the granularity of the analysis is at the platform or group level and the diagnosis results cannot be applied to the individual application. To address this issue, we designed and developed a method named “Artificial Intelligence for I/O” (AIIO), which uses AI and its interpretation technology to diagnose I/O performance bottlenecks at the job level automatically. By considering the sparsity of I/O log files, employing multiple AI models for performance prediction, merging diagnosis results across multiple models, and generalizing its performance prediction and diagnosis functions, AIIO can accurately and robustly identify the bottleneck of an even unseen application. Experimental results show that real and unseen applications can use the diagnosis results from AIIO to improve their I/O performance by at most 146 × .

AIIO: Using Artificial Intelligence for Job-Level and Automatic I/O Performance Bottleneck Diagnosis

The complex software and hardware I/O stack of HPC platforms makes it challenging for end-users to obtain superior I/O performance and to understand the root causes of I/O bottlenecks they encounter. Despite the continuous efforts from the community to profile I/O performance and propose new optimization techniques and tuning options for improving the performance, there is still a translation gap between profiling and tuning. In this paper, we propose Drishti, a solution to guide scientists in optimizing I/O in their applications by detecting typical I/O performance pitfalls and providing recommendations. We illustrate its applicability in two case studies and evaluate its robustness and performance by summarizing the issues detected in over a hundred thousand Darshan logs collected on the Cori supercomputer at the National Energy Research Scientific Computing Center (NERSC). Drishti can empower end-users and guide them in the I/O optimization journey by shedding some light on everyday I/O performance pitfalls and how to fix them.

Drishti: Guiding End-Users in the I/O Optimization Journey

The existing parallel I/O stack is complex and difficult to tune due to the interdependencies among multiple factors that impact the performance of data movement between storage and compute systems. When performance is slower than expected, end-users, developers, and system administrators rely on I/O profiling and tracing information to pinpoint the root causes of inefficiencies. Despite having numerous tools that collect I/O metrics on production systems, it is not obvious where the I/O bottlenecks are (unless one is an I/O expert), their root causes, and what to do to solve them. Hence, there is a gap between the currently available metrics, the issues they represent, and the application of optimizations that would mitigate performance slowdowns. An I/O specialist often checks for common problems before diving into the specifics of each application and workload. Streamlining such analysis, investigation, and recommendations could close this gap without requiring a specialist to intervene in every case. In this paper, we propose a novel interactive, user-oriented visualization, and analysis framework, called Drishti. This framework helps users to pinpoint various root causes of I/O performance problems and to provide a set of actionable recommendations for improving performance based on the observed characteristics of an application. We evaluate the applicability and correctness of Drishti using four use cases from distinct science domains and demonstrate its value to end-users, developers, and system administrators when seeking to improve an application’s I/O performance. 

Illuminating the I/O Optimization Path of Scientific Applications

Application in multimedia: from camera to VR

Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par, Alpaka, and OpenMP/OpenACC, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats.
We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.

pdf/application-of-performance-portability-solutions-for-gpus-4rjupya91t.pdf

Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels

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Drishti: Guiding End-Users in the I/O Optimization Journey

Illuminating the I/O Optimization Path of Scientific Applications

Application in multimedia: from camera to VR

Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels