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Register a new userCROFT: A scalable three-dimensional parallel Fast Fourier Transform (FFT) implementation for High Performance Clusters
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Added by
Vivek Gavane
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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The FFT of three-dimensional (3D) input data is an important computational kernel of numerical simulations and is widely used in High Performance Computing (HPC) codes running on a large number of processors. Performance of many scientific applications such as Molecular Dynamic simulations depends on the underlying 3D parallel FFT library being used. In this paper, we present C-DACs three-dimensional Fast Fourier Transform (CROFT) library which implements three-dimensional parallel FFT using pencil decomposition. To exploit the hyperthreading capabilities of processor cores without affecting performance, CROFT is designed to use multithreading along with MPI. CROFT implementation has an innovative feature of overlapping compute and memory-I/O with MPI communication using multithreading. As opposed to other 3D FFT implementations, CROFT uses only two threads where one thread is dedicated for communication so that it can be effectively overlapped with computations. Thus, depending on the number of processes used, CROFT achieves performance improvement of about 51% to 42% as compared to FFTW3 library.
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Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite the advantages of modularity and elasticity microservices offer, they also complicate cluster management and performance debugging, as dependencies between tiers introduce backpressure and cascading QoS violations. We present Sage, a machine learning-driven root cause analysis system for interactive cloud microservices. Sage leverages unsupervised ML models to circumvent the overhead of trace labeling, captures the impact of dependencies between microservices to determine the root cause of unpredictable performance online, and applies corrective actions to recover a cloud services QoS. In experiments on both dedicated local clusters and large clusters on Google Compute Engine we show that Sage consistently achieves over 93% accuracy in correctly identifying the root cause of QoS violations, and improves performance predictability.
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Bayesian optimization (BO) is a flexible and powerful framework that is suitable for computationally expensive simulation-based applications and guarantees statistical convergence to the global optimum. While remaining as one of the most popular optimization methods, its capability is hindered by the size of data, the dimensionality of the considered problem, and the nature of sequential optimization. These scalability issues are intertwined with each other and must be tackled simultaneously. In this work, we propose the Scalable$^3$-BO framework, which employs sparse GP as the underlying surrogate model to scope with Big Data and is equipped with a random embedding to efficiently optimize high-dimensional problems with low effective dimensionality. The Scalable$^3$-BO framework is further leveraged with asynchronous parallelization feature, which fully exploits the computational resource on HPC within a computational budget. As a result, the proposed Scalable$^3$-BO framework is scalable in three independent perspectives: with respect to data size, dimensionality, and computational resource on HPC. The goal of this work is to push the frontiers of BO beyond its well-known scalability issues and minimize the wall-clock waiting time for optimizing high-dimensional computationally expensive applications. We demonstrate the capability of Scalable$^3$-BO with 1 million data points, 10,000-dimensional problems, with 20 concurrent workers in an HPC environment.
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