Accelerating Climate and Weather Simulations through Hybrid Computing

Unconventional multi- and many-core processors (e.g. IBM (R) Cell B.E.(TM) and NVIDIA (R) GPU) have emerged as effective accelerators in trial climate and weather simulations. Yet these climate and weather models typically run on parallel computers with conventional processors (e.g. Intel, AMD, and IBM) using Message Passing Interface. To address challenges involved in efficiently and easily connecting accelerators to parallel computers, we investigated using IBM's Dynamic Application Virtualization (TM) (IBM DAV) software in a prototype hybrid computing system with representative climate and weather model components. The hybrid system comprises two Intel blades and two IBM QS22 Cell B.E. blades, connected with both InfiniBand(R) (IB) and 1-Gigabit Ethernet. The system significantly accelerates a solar radiation model component by offloading compute-intensive calculations to the Cell blades. Systematic tests show that IBM DAV can seamlessly offload compute-intensive calculations from Intel blades to Cell B.E. blades in a scalable, load-balanced manner. However, noticeable communication overhead was observed, mainly due to IP over the IB protocol. Full utilization of IB Sockets Direct Protocol and the lower latency production version of IBM DAV will reduce this overhead

An analysis of the feasibility and benefits of GPU/multicore acceleration of the Weather Research and Forecasting model

There is a growing need for ever more accurate climate and weather simulations to be delivered in shorter timescales, in particular, to guard against severe weather events such as hurricanes and heavy rainfall. Due to climate change, the severity and frequency of such events – and thus the economic impact – are set to rise dramatically. Hardware acceleration using graphics processing units (GPUs) or Field-Programmable Gate Arrays (FPGAs) could potentially result in much reduced run times or higher accuracy simulations. In this paper, we present the results of a study of the Weather Research and Forecasting (WRF) model undertaken in order to assess if GPU and multicore acceleration of this type of numerical weather prediction (NWP) code is both feasible and worthwhile. The focus of this paper is on acceleration of code running on a single compute node through offloading of parts of the code to an accelerator such as a GPU. The governing equations set of the WRF model is based on the compressible, non-hydrostatic atmospheric motion with multi-physics processes. We put this work into context by discussing its more general applicability to multi-physics fluid dynamics codes: in many fluid dynamics codes, the numerical schemes of the advection terms are based on finite differences between neighboring cells, similar to the WRF code. For fluid systems including multi-physics processes, there are many calls to these advection routines. This class of numerical codes will benefit from hardware acceleration. We studied the performance of the original code of the WRF model and proposed a simple model for comparing multicore CPU and GPU performance. Based on the results of extensive profiling of representative WRF runs, we focused on the acceleration of the scalar advection module. We discuss the implementation of this module as a data-parallel kernel in both OpenCL and OpenMP. We show that our data-parallel kernel version of the scalar advection module runs up to seven times faster on the GPU compared with the original code on the CPU. However, as the data transfer cost between GPU and CPU is very high (as shown by our analysis), there is only a small speed-up (two times) for the fully integrated code. We show that it would be possible to offset the data transfer cost through GPU acceleration of a larger portion of the dynamics code. In order to carry out this research, we also developed an extensible software system for integrating OpenCL code into large Fortran code bases such as WRF. This is one of the main contributions of our work. We discuss the system to show how it allows the replacement of the sections of the original codebase with their OpenCL counterparts with minimal changes – literally only a few lines – to the original code. Our final assessment is that, even with the current system architectures, accelerating WRF – and hence also other, similar types of multi-physics fluid dynamics codes – with a factor of up to five times is definitely an achievable goal. Accelerating multi-physics fluid dynamics codes including NWP codes is vital for its application to weather forecasting, environmental pollution warning, and emergency response to the dispersion of hazardous materials. Implementing hardware acceleration capability for fluid dynamics and NWP codes is a prerequisite for up-to-date and future computer architectures

What does fault tolerant Deep Learning need from MPI?

Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed DL implementations which use MPI require days of training (model learning) time on commonly studied datasets. Long running DL applications become susceptible to faults - requiring development of a fault tolerant system infrastructure, in addition to fault tolerant DL algorithms. This raises an important question: What is needed from MPI for de- signing fault tolerant DL implementations? In this paper, we address this problem for permanent faults. We motivate the need for a fault tolerant MPI specification by an in-depth consideration of recent innovations in DL algorithms and their properties, which drive the need for specific fault tolerance features. We present an in-depth discussion on the suitability of different parallelism types (model, data and hybrid); a need (or lack thereof) for check-pointing of any critical data structures; and most importantly, consideration for several fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI and their applicability to fault tolerant DL implementations. We leverage a distributed memory implementation of Caffe, currently available under the Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches by ex- tending MaTEx-Caffe for using ULFM-based implementation. Our evaluation using the ImageNet dataset and AlexNet, and GoogLeNet neural network topologies demonstrates the effectiveness of the proposed fault tolerant DL implementation using OpenMPI based ULFM

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Climate Informatics: Accelerating Discovering in Climate Science with Machine Learning

The goal of climate informatics, an emerging discipline, is to inspire collaboration between climate scientists and data scientists, in order to develop tools to analyze complex and ever-growing amounts of observed and simulated climate data, and thereby bridge the gap between data and understanding. Here, recent climate informatics work is presented, along with details of some of the field's remaining challenges. Given the impact of climate change, understanding the climate system is an international priority. The goal of climate informatics is to inspire collaboration between climate scientists and data scientists, in order to develop tools to analyze complex and ever-growing amounts of observed and simulated climate data, and thereby bridge the gap between data and understanding. Here, recent climate informatics work is presented, along with details of some of the remaining challenges