Optimizing Collective Communication for Scalable Scientific Computing and Deep Learning

In the realm of distributed computing, collective operations involve coordinated communication and synchronization among multiple processing units, enabling efficient data exchange and collaboration. Scientific applications, such as simulations, computational fluid dynamics, and scalable deep learning, require complex computations that can be parallelized across multiple nodes in a distributed system. These applications often involve data-dependent communication patterns, where collective operations are critical for achieving high performance in data exchange. Optimizing collective operations for scientific applications and deep learning involves improving the algorithms, communication patterns, and data distribution strategies to minimize communication overhead and maximize computational efficiency. Within the context of this dissertation, the specific focus is on optimizing the alltoall operation in 3D Fast Fourier Transform (FFT) applications and the allreduce operation in parallel deep learning, particularly on High-Performance Computing (HPC) systems. Advanced communication algorithms and methods are explored and implemented to improve communication efficiency, consequently enhancing the overall performance of 3D FFT applications. Furthermore, this dissertation investigates the identification of performance bottlenecks during collective communication over Horovod on distributed systems. These bottlenecks are addressed by proposing an optimized parallel communication pattern specifically tailored to alleviate the aforementioned limitations during the training phase in distributed deep learning. The objective is to achieve faster convergence and improve the overall training efficiency. Moreover, this dissertation proposes fault tolerance and elastic scaling features for distributed deep learning by leveraging the User-Level Failure Mitigation (ULFM) from Message Passing Interface (MPI). By incorporating ULFM MPI, the dissertation aims to enhance the elastic capabilities of distributed deep learning systems. This approach enables graceful and lightweight handling of failures while facilitating seamless scaling in dynamic computing environments

Sustainable HPC: Modeling, Characterization, and Implications of Carbon Footprint in Modern HPC Systems

The rapid growth in demand for HPC systems has led to a rise in energy consumption and carbon emissions, which requires urgent intervention. In this work, we present a comprehensive framework for analyzing the carbon footprint of high-performance computing (HPC) systems, considering the carbon footprint during both the hardware production and system operational stages. Our work employs HPC hardware component carbon footprint modeling, regional carbon intensity analysis, and experimental characterization of the system life cycle to highlight the importance of quantifying the carbon footprint of an HPC system holistically

Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research

Cancer is a complex disease, the understanding and treatment of which are being aided through increases in the volume of collected data and in the scale of deployed computing power. Consequently, there is a growing need for the development of data-driven and, in particular, deep learning methods for various tasks such as cancer diagnosis, detection, prognosis, and prediction. Despite recent successes, however, designing high-performing deep learning models for nonimage and nontext cancer data is a time-consuming, trial-and-error, manual task that requires both cancer domain and deep learning expertise. To that end, we develop a reinforcement-learning-based neural architecture search to automate deep-learning-based predictive model development for a class of representative cancer data. We develop custom building blocks that allow domain experts to incorporate the cancer-data-specific characteristics. We show that our approach discovers deep neural network architectures that have significantly fewer trainable parameters, shorter training time, and accuracy similar to or higher than those of manually designed architectures. We study and demonstrate the scalability of our approach on up to 1,024 Intel Knights Landing nodes of the Theta supercomputer at the Argonne Leadership Computing Facility.Comment: SC '19: IEEE/ACM International Conference on High Performance Computing, Networking, Storage and Analysis, November 17--22, 2019, Denver, C

Power Bounded Computing on Current & Emerging HPC Systems

Power has become a critical constraint for the evolution of large scale High Performance Computing (HPC) systems and commercial data centers. This constraint spans almost every level of computing technologies, from IC chips all the way up to data centers due to physical, technical, and economic reasons. To cope with this reality, it is necessary to understand how available or permissible power impacts the design and performance of emergent computer systems. For this reason, we propose power bounded computing and corresponding technologies to optimize performance on HPC systems with limited power budgets. We have multiple research objectives in this dissertation. They center on the understanding of the interaction between performance, power bounds, and a hierarchical power management strategy. First, we develop heuristics and application aware power allocation methods to improve application performance on a single node. Second, we develop algorithms to coordinate power across nodes and components based on application characteristic and power budget on a cluster. Third, we investigate performance interference induced by hardware and power contentions, and propose a contention aware job scheduling to maximize system throughput under given power budgets for node sharing system. Fourth, we extend to GPU-accelerated systems and workloads and develop an online dynamic performance & power approach to meet both performance requirement and power efficiency. Power bounded computing improves performance scalability and power efficiency and decreases operation costs of HPC systems and data centers. This dissertation opens up several new ways for research in power bounded computing to address the power challenges in HPC systems. The proposed power and resource management techniques provide new directions and guidelines to green exscale computing and other computing systems

Understanding Patterns of Deep Learning ModelEvolution in Network Architecture Search

Network Architecture Search and specifically Regularized Evolution is a common way to refine the structure of a deep learning model.However, little is known about how models empirically evolve over time which has design implications for designing caching policies, refining the search algorithm for particular applications, and other important use cases.In this work, we algorithmically analyze and quantitatively characterize the patterns of model evolution for a set of models from the Candle project and the Nasbench-201 search space.We show how the evolution of the model structure is influenced by the regularized evolution algorithm. We describe how evolutionary patterns appear in distributed settings and opportunities for caching and improved scheduling. Lastly, we describe the conditions that affect when particular model architectures rise and fall in popularity based on their frequency of acting as a donor in a sliding window.Comment: 11 pages, 4 figure

J Biomed Inform

Objective:In machine learning, it is evident that the classification of the task performance increases if bootstrap aggregation (bagging) is applied. However, the bagging of deep neural networks takes tremendous amounts of computational resources and training time. The research question that we aimed to answer in this research is whether we could achieve higher task performance scores and accelerate the training by dividing a problem into sub-problems.Materials and Methods:The data used in this study consist of free text from electronic cancer pathology reports. We applied bagging and partitioned data training using Multi-Task Convolutional Neural Network (MT-CNN) and Multi-Task Hierarchical Convolutional Attention Network (MT-HCAN) classifiers. We split a big problem into 20 sub-problems, resampled the training cases 2,000 times, and trained the deep learning model for each bootstrap sample and each sub-problem\u2014thus, generating up to 40,000 models. We performed the training of many models concurrently in a high-performance computing environment at Oak Ridge National Laboratory (ORNL).Results:We demonstrated that aggregation of the models improves task performance compared with the single-model approach, which is consistent with other research studies; and we demonstrated that the two proposed partitioned bagging methods achieved higher classification accuracy scores on four tasks. Notably, the improvements were significant for the extraction of cancer histology data, which had more than 500 class labels in the task; these results show that data partition may alleviate the complexity of the task. On the contrary, the methods did not achieve superior scores for the tasks of site and subsite classification. Intrinsically, since data partitioning was based on the primary cancer site, the accuracy depended on the determination of the partitions, which needs further investigation and improvement.Conclusion:Results in this research demonstrate that 1. The data partitioning and bagging strategy achieved higher performance scores. 2. We achieved faster training leveraged by the high-performance Summit supercomputer at ORNL.20202021-01-13T00:00:00ZHHSN261201800013C/CA/NCI NIH HHSUnited States/HHSN261201800016C/CA/NCI NIH HHSUnited States/U58 DP003907/DP/NCCDPHP CDC HHSUnited States/HHSN261201800007C/CA/NCI NIH HHSUnited States/P30 CA177558/CA/NCI NIH HHSUnited States/HHSN261201300021C/CA/NCI NIH HHSUnited States/HHSN261201800013I/CA/NCI NIH HHSUnited States/P30 CA042014/CA/NCI NIH HHSUnited States/32919043PMC82765801002

MISO: Exploiting Multi-Instance GPU Capability on Multi-Tenant Systems for Machine Learning

GPU technology has been improving at an expedited pace in terms of size and performance, empowering HPC and AI/ML researchers to advance the scientific discovery process. However, this also leads to inefficient resource usage, as most GPU workloads, including complicated AI/ML models, are not able to utilize the GPU resources to their fullest extent -- encouraging support for GPU multi-tenancy. We propose MISO, a technique to exploit the Multi-Instance GPU (MIG) capability on the latest NVIDIA datacenter GPUs (e.g., A100, H100) to dynamically partition GPU resources among co-located jobs. MISO's key insight is to use the lightweight, more flexible Multi-Process Service (MPS) capability to predict the best MIG partition allocation for different jobs, without incurring the overhead of implementing them during exploration. Due to its ability to utilize GPU resources more efficiently, MISO achieves 49% and 16% lower average job completion time than the unpartitioned and optimal static GPU partition schemes, respectively

Approachable Error Bounded Lossy Compression

Compression is commonly used in HPC applications to move and store data. Traditional lossless compression, however, does not provide adequate compression of floating point data often found in scientific codes. Recently, researchers and scientists have turned to lossy compression techniques that approximate the original data rather than reproduce it in order to achieve desired levels of compression. Typical lossy compressors do not bound the errors introduced into the data, leading to the development of error bounded lossy compressors (EBLC). These tools provide the desired levels of compression as mathematical guarantees on the errors introduced. However, the current state of EBLC leaves much to be desired. The existing EBLC all have different interfaces requiring codes to be changed to adopt new techniques; EBLC have many more configuration options than their predecessors, making them more difficult to use; and EBLC typically bound quantities like point wise errors rather than higher level metrics such as spectra, p-values, or test statistics that scientists typically use. My dissertation aims to provide a uniform interface to compression and to develop tools to allow application scientists to understand and apply EBLC. This dissertation proposal presents three groups of work: LibPressio, a standard interface for compression and analysis; FRaZ/LibPressio-Opt frameworks for the automated configuration of compressors using LibPressio; and work on tools for analyzing errors in particular domains