Scientific Practice and Democratic Virtues

Chapter 21: Scientific Practice and Democratic Values Democracy and science, it might be thought, go hand in hand. As we commonly think of it, science has its roots in the heritage of Greek thought, and the same is true of democracy. And while Athenian democracy developed at best a complicated relationship with its intellectual luminaries, Athens was nevertheless home to both Plato’s Academy and Aristotle’s Lyceum. Nearly two centuries later, the scientific revolution most quickly took root in those nations that had come to have some semblance of democratic rule. While the Republic of Florence had passed under Medici control by the time Galileo was active, Newton’s home was then the most democratic nation on earth. During the Cold War, the scientific achievements of the democratic West were often put forth as signs of its superiority over its communist foes, and the existence of Soviet scientist-dissidents such as Andrei Sakharov supported the narrative that the scientific enterprise, necessarily committed to the free exchange of ideas and the pursuit of truth even when socially inconvenient, simply cannot thrive in more oppressive contexts.The truth is admittedly more complicated: scientists aided and sometimes enthusiastically abetted the Nazi regime, and ongoing nuclear proliferation occurs only because of the many scientists—not all of them coerced—willing to contribute to such efforts. But such cases seem to be the exceptions, and they generally involve not the pursuit of science but its technological application. Genuine science plausibly needs, if not democracy, at least adequate freedom of expression in order for ideas to be communicated and critiqued. This was true enough in Galileo’s day that scholars had access not only to the great works of the past but also, with some effort, the works of contemporaries.[2] On this account, if open, democratic regimes do not actively cause the flourishing of science, they at least do not suppress it, enabling the possibility of cumulative success over time.But the arrow of causality may also run in the other direction. Democracy may not strictly need science, but it may be the case that the scientific mindset contributes to the quality and integrity of democratic forms of government. Indeed, one of the most persistent correlates to the emergence of democracy is the level of its citizens’ education, a point first noted by Seymour Lipset in 1959 and which continues to have significant support today.[3] Importantly, education is not the same as scientific education, and it is quite possible that other features of education, including relevant components of civic education, account for the relationship. Even so, it is plausible to think that scientific thinking and therefore scientific training contribute to the quality and integrity of democratic regimes, and do so beyond the merely instrumental material benefits that science provides

Can One Love the Distant Other? Empathy, Affiliation, and Cosmopolitanism

An ongoing debate in political and moral philosophy concerns the nature of international obligations. While cosmopolitans argue that duties of justice are independent of national borders, statists argue otherwise, sometimes basing their account on the limitations of our empathic concern, a line of argument found much earlier in Adam Smith. Although critics argue that empathy is neither necessary nor sufficient for morality, and although statists imply that psychological limitations of the kind that would be based in empathy prevent the realization of commitments to distant others beyond humanitarian aid, I argue that both these views are incorrect. While the possession of cognitive and emotional empathy is clearly not sufficient for being moral, the requirement for cognitive empathy arises out of a proper understanding of moral functioning, and the need for emotional empathy arises out of a natural necessity due to the kind of affiliative, biological creatures that we are. Since our capacities for cognitive and emotional empathy are not simply innately given but capable of being shaped by processes of learning and culture, statist arguments against stronger moral obligations across nations are poorly founded

PaPaS: A Portable, Lightweight, and Generic Framework for Parallel Parameter Studies

The current landscape of scientific research is widely based on modeling and simulation, typically with complexity in the simulation's flow of execution and parameterization properties. Execution flows are not necessarily straightforward since they may need multiple processing tasks and iterations. Furthermore, parameter and performance studies are common approaches used to characterize a simulation, often requiring traversal of a large parameter space. High-performance computers offer practical resources at the expense of users handling the setup, submission, and management of jobs. This work presents the design of PaPaS, a portable, lightweight, and generic workflow framework for conducting parallel parameter and performance studies. Workflows are defined using parameter files based on keyword-value pairs syntax, thus removing from the user the overhead of creating complex scripts to manage the workflow. A parameter set consists of any combination of environment variables, files, partial file contents, and command line arguments. PaPaS is being developed in Python 3 with support for distributed parallelization using SSH, batch systems, and C++ MPI. The PaPaS framework will run as user processes, and can be used in single/multi-node and multi-tenant computing systems. An example simulation using the BehaviorSpace tool from NetLogo and a matrix multiply using OpenMP are presented as parameter and performance studies, respectively. The results demonstrate that the PaPaS framework offers a simple method for defining and managing parameter studies, while increasing resource utilization.Comment: 8 pages, 6 figures, PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

GASPRNG: GPU accelerated scalable parallel random number generator library

AbstractGraphics processors represent a promising technology for accelerating computational science applications. Many computational science applications require fast and scalable random number generation with good statistical properties, so they use the Scalable Parallel Random Number Generators library (SPRNG). We present the GPU Accelerated SPRNG library (GASPRNG) to accelerate SPRNG in GPU-based high performance computing systems. GASPRNG includes code for a host CPU and CUDA code for execution on NVIDIA graphics processing units (GPUs) along with a programming interface to support various usage models for pseudorandom numbers and computational science applications executing on the CPU, GPU, or both. This paper describes the implementation approach used to produce high performance and also describes how to use the programming interface. The programming interface allows a user to be able to use GASPRNG the same way as SPRNG on traditional serial or parallel computers as well as to develop tightly coupled programs executing primarily on the GPU. We also describe how to install GASPRNG and use it. To help illustrate linking with GASPRNG, various demonstration codes are included for the different usage models. GASPRNG on a single GPU shows up to 280x speedup over SPRNG on a single CPU core and is able to scale for larger systems in the same manner as SPRNG. Because GASPRNG generates identical streams of pseudorandom numbers as SPRNG, users can be confident about the quality of GASPRNG for scalable computational science applications.Program summaryProgram title: GASPRNGCatalogue identifier: AEOI_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOI_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: UTK license.No. of lines in distributed program, including test data, etc.: 167900No. of bytes in distributed program, including test data, etc.: 1422058Distribution format: tar.gzProgramming language: C and CUDA.Computer: Any PC or workstation with NVIDIA GPU (Tested on Fermi GTX480, Tesla C1060, Tesla M2070).Operating system: Linux with CUDA version 4.0 or later. Should also run on MacOS, Windows, or UNIX.Has the code been vectorized or parallelized?: Yes. Parallelized using MPI directives.RAM: 512 MB∼ 732 MB (main memory on host CPU, depending on the data type of random numbers.) / 512 MB (GPU global memory)Classification: 4.13, 6.5.Nature of problem:Many computational science applications are able to consume large numbers of random numbers. For example, Monte Carlo simulations are able to consume limitless random numbers for the computation as long as resources for the computing are supported. Moreover, parallel computational science applications require independent streams of random numbers to attain statistically significant results. The SPRNG library provides this capability, but at a significant computational cost. The GASPRNG library presented here accelerates the generators of independent streams of random numbers using graphical processing units (GPUs).Solution method:Multiple copies of random number generators in GPUs allow a computational science application to consume large numbers of random numbers from independent, parallel streams. GASPRNG is a random number generators library to allow a computational science application to employ multiple copies of random number generators to boost performance. Users can interface GASPRNG with software code executing on microprocessors and/or GPUs.Running time:The tests provided take a few minutes to run