Experience with a software engineering environment framework

Experience with a software engineering environment framework tool called the Automated Product Control Environment (APCE) is described. The goals of the framework design, an overview of the major functions and features of the framework, and implementation and use of the framework are presented. Aspects of the framework discussed include automation and control; portability, distributability, and interoperability; cost/benefit analysis; and productivity. Results of using the framework are discussed and the framework approach is briefly compared to other software development environment approaches

Taking It With You: Platform Barriers to Entry and the Limits of Data Portability

Policymakers are faced with a vexing problem: how to increase competition in a tech sector dominated by a few giants. One answer proposed and adopted by regulators in the United States and abroad is to require large platforms to allow consumers to move their data from one platform to another, an approach known as data portability. Facebook, Google, Apple, and other major tech companies have enthusiastically supported data portability through their own technical and political initiatives. Today, data portability has taken hold as one of the go-to solutions to address the tech industry’s competition concerns. This Article argues that despite the regulatory and industry alliance around data portability, today’s public and private data portability efforts are unlikely to meaningfully improve competition. This is because current portability efforts focus solely on mitigating switching costs, ignoring other barriers to entry that may preclude new platforms from entering the market. The technical implementations of data portability encouraged by existing regulation—namely one-off exports and API interoperability—address switching costs but not the barriers of network effects, unique data access, and economies of scale. This Article proposes a new approach to better alleviate these other barriers called collective portability, which would allow groups of users to coordinate to transfer data they share to a new platform, all at once. Although not a panacea, collective portability would provide a meaningful alternative to existing approaches while avoiding both the privacy/competitive utility trade off of one-off exports and the hard-to regulate power dynamics of APIs

A Performance-Portable SYCL Implementation of CRK-HACC for Exascale

The first generation of exascale systems will include a variety of machine architectures, featuring GPUs from multiple vendors. As a result, many developers are interested in adopting portable programming models to avoid maintaining multiple versions of their code. It is necessary to document experiences with such programming models to assist developers in understanding the advantages and disadvantages of different approaches. To this end, this paper evaluates the performance portability of a SYCL implementation of a large-scale cosmology application (CRK-HACC) running on GPUs from three different vendors: AMD, Intel, and NVIDIA. We detail the process of migrating the original code from CUDA to SYCL and show that specializing kernels for specific targets can greatly improve performance portability without significantly impacting programmer productivity. The SYCL version of CRK-HACC achieves a performance portability of 0.96 with a code divergence of almost 0, demonstrating that SYCL is a viable programming model for performance-portable applications.Comment: 12 pages, 13 figures, 2023 International Workshop on Performance, Portability & Productivity in HP

The Scalability-Efficiency/Maintainability-Portability Trade-off in Simulation Software Engineering: Examples and a Preliminary Systematic Literature Review

Large-scale simulations play a central role in science and the industry. Several challenges occur when building simulation software, because simulations require complex software developed in a dynamic construction process. That is why simulation software engineering (SSE) is emerging lately as a research focus. The dichotomous trade-off between scalability and efficiency (SE) on the one hand and maintainability and portability (MP) on the other hand is one of the core challenges. We report on the SE/MP trade-off in the context of an ongoing systematic literature review (SLR). After characterizing the issue of the SE/MP trade-off using two examples from our own research, we (1) review the 33 identified articles that assess the trade-off, (2) summarize the proposed solutions for the trade-off, and (3) discuss the findings for SSE and future work. Overall, we see evidence for the SE/MP trade-off and first solution approaches. However, a strong empirical foundation has yet to be established; general quantitative metrics and methods supporting software developers in addressing the trade-off have to be developed. We foresee considerable future work in SSE across scientific communities.Comment: 9 pages, 2 figures. Accepted for presentation at the Fourth International Workshop on Software Engineering for High Performance Computing in Computational Science and Engineering (SEHPCCSE 2016

Analogy Mining for Specific Design Needs

Finding analogical inspirations in distant domains is a powerful way of solving problems. However, as the number of inspirations that could be matched and the dimensions on which that matching could occur grow, it becomes challenging for designers to find inspirations relevant to their needs. Furthermore, designers are often interested in exploring specific aspects of a product-- for example, one designer might be interested in improving the brewing capability of an outdoor coffee maker, while another might wish to optimize for portability. In this paper we introduce a novel system for targeting analogical search for specific needs. Specifically, we contribute a novel analogical search engine for expressing and abstracting specific design needs that returns more distant yet relevant inspirations than alternate approaches

Are Accuracy and Robustness Correlated?

Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors. In this paper, we perform experiments on various adversarial example generation approaches with multiple deep convolutional neural networks including Residual Networks, the best performing models on ImageNet Large-Scale Visual Recognition Challenge 2015. We compare the adversarial example generation techniques with respect to the quality of the produced images, and measure the robustness of the tested machine learning models to adversarial examples. Finally, we conduct large-scale experiments on cross-model adversarial portability. We find that adversarial examples are mostly transferable across similar network topologies, and we demonstrate that better machine learning models are less vulnerable to adversarial examples.Comment: Accepted for publication at ICMLA 201

Learning from the Success of MPI

The Message Passing Interface (MPI) has been extremely successful as a portable way to program high-performance parallel computers. This success has occurred in spite of the view of many that message passing is difficult and that other approaches, including automatic parallelization and directive-based parallelism, are easier to use. This paper argues that MPI has succeeded because it addresses all of the important issues in providing a parallel programming model.Comment: 12 pages, 1 figur