ParaDRAM：一个用于并行高性能延迟拒绝自适应大都会马尔可夫链蒙特卡罗模拟的跨语言编译器
ParaDRAM: A Cross-Language Toolbox for Parallel High-Performance Delayed-Rejection Adaptive Metropolis Markov Chain Monte Carlo Simulations
摘要：
我们推出了ParaDRAM，这是一款高性能并行延迟拒绝自适应大都会马尔可夫链蒙特卡罗软件，用于科学推理中遇到的数学目标函数的优化、采样和集成。ParaDRAM目前可以从几种流行的程序设计语言访问，包括C/C++，Fortran，MATLAB，Python，并且是ParaMonte开源项目的一部分，具有以下主要设计目标：1.蒙特卡洛模拟的全自动化，2。核心库与尽可能多的程序设计语言的互操作性，从而提供跨所有程序设计语言的统一应用程序编程接口和蒙特卡罗模拟环境，3.高性能4.从个人笔记本电脑到超级计算机的模拟的并行性和可扩展性，5。对外部库的依赖几乎为零，6.模拟的完全确定性再现性，7.仿真结果的自动综合报告和后处理。我们提出并讨论了几种新的技术，在ParaDRAM中实现自动和动态地确保良好的混合和减少适应所产生的伪马尔可夫链从ParaDRAM。我们还讨论了一个有效的数据存储方法中使用的ParaDRAM，减少了平均内存和存储要求的算法，简单的模拟问题的一个因素为4，一个数量级和更多的采样复杂的高维数学目标函数的实现。最后，我们讨论了ParaDRAM的设计目标如何帮助用户在各种计算平台上轻松有效地解决各种机器学习和科学推理问题。
英文摘要：
We present ParaDRAM, a high-performance Parallel Delayed-Rejection Adaptive Metropolis Markov Chain Monte Carlo software for optimization, sampling, and integration of mathematical objective functions encountered in scientific inference. ParaDRAM is currently accessible from several popular programming languages including C/C++, Fortran, MATLAB, Python and is part of the ParaMonte open-source project with the following principal design goals: 1. full automation of Monte Carlo simulations, 2. interoperability of the core library with as many programming languages as possible, thus, providing a unified Application Programming Interface and Monte Carlo simulation environment across all programming languages, 3. high-performance 4. parallelizability and scalability of simulations from personal laptops to supercomputers, 5. virtually zero-dependence on external libraries, 6. fully-deterministic reproducibility of simulations, 7. automatic comprehensive reporting and post-processing of the simulation results. We present and discuss several novel techniques implemented in ParaDRAM to automatically and dynamically ensure the good-mixing and the diminishing-adaptation of the resulting pseudo-Markov chains from ParaDRAM. We also discuss the implementation of an efficient data storage method used in ParaDRAM that reduces the average memory and storage requirements of the algorithm by, a factor of 4 for simple simulation problems, to an order of magnitude and more for sampling complex high-dimensional mathematical objective functions. Finally, we discuss how the design goals of ParaDRAM can help users readily and efficiently solve a variety of machine learning and scientific inference problems on a wide range of computing platforms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
