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1. Predicting and Comparing the Performance of Array Management Libraries

   https://doi.org/10.1109/IPDPS47924.2020.00097  

   Kang, Donghe; Rubel, Oliver; Byna, Suren; Blanas, Spyros (May 2020, 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS))

   Many applications are increasingly becoming I/O-bound. To improve scalability, analytical models of parallel I/O performance are often consulted to determine possible I/O optimizations. However, I/O performance modeling has predominantly focused on applications that directly issue I/O requests to a parallel file system or a local storage device. These I/O models are not directly usable by applications that access data through standardized I/O libraries, such as HDF5, FITS, and NetCDF, because a single I/O request to an object can trigger a cascade of I/O operations to different storage blocks. The I/O performance characteristics of applications that rely on these libraries is a complex function of the underlying data storage model, user-configurable parameters and object-level access patterns. As a consequence, I/O optimization is predominantly an ad-hoc process that is performed by application developers, who are often domain scientists with limited desire to delve into nuances of the storage hierarchy of modern computers.This paper presents an analytical cost model to predict the end-to-end execution time of applications that perform I/O through established array management libraries. The paper focuses on the HDF5 and Zarr array libraries, as examples of I/O libraries with radically different storage models: HDF5 stores every object in one file, while Zarr creates multiple files to store different objects. We find that accessing array objects via these I/O libraries introduces new overheads and optimizations. Specifically, in addition to I/O time, it is crucial to model the cost of transforming data to a particular storage layout (memory copy cost), as well as model the benefit of accessing a software cache. We evaluate the model on real applications that process observations (neuroscience) and simulation results (plasma physics). The evaluation on three HPC clusters reveals that I/O accounts for as little as 10% of the execution time in some cases, and hence models that only focus on I/O performance cannot accurately capture the performance of applications that use standard array storage libraries. In parallel experiments, our model correctly predicts the fastest storage library between HDF5 and Zarr 94% of the time, in contrast with 70% of the time for a cutting-edge I/O model. 

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2. SHMEM-ML: Leveraging OpenSHMEM and Apache Arrow for Scalable, Composable Machine Learning

   https://doi.org/10.1007/978-3-031-04888-3_7  

   Grossman, Max; Poole, Steve; Pritchard, Howard; Sarkar, Vivek (May 2022, Lecture notes in computer science)

   Poole, Steve; Hernandez, Oscar; Baker, Matthew; Curtis, Tony (Ed.)

   SHMEM-ML is a domain specific library for distributed array computations and machine learning model training & inference. Like other projects at the intersection of machine learning and HPC (e.g. dask, Arkouda, Legate Numpy), SHMEM-ML aims to leverage the performance of the HPC software stack to accelerate machine learning workflows. However, it differs in a number of ways. First, SHMEM-ML targets the full machine learning workflow, not just model training. It supports a general purpose nd-array abstraction commonly used in Python machine learning applications, and efficiently distributes transformation and manipulation of this ndarray across the full system. Second, SHMEM-ML uses OpenSHMEM as its underlying communication layer, enabling high performance networking across hundreds or thousands of distributed processes. While most past work in high performance machine learning has leveraged HPC message passing communication models as a way to efficiently exchange model gradient updates, SHMEM-ML’s focus on the full machine learning lifecycle means that a more flexible and adaptable communication model is needed to support both fine and coarse grain communication. Third, SHMEM-ML works to interoperate with the broader Python machine learning software ecosystem. While some frameworks aim to rebuild that ecosystem from scratch on top of the HPC software stack, SHMEM-ML is built on top of Apache Arrow, an in-memory standard for data formatting and data exchange between libraries. This enables SHMEM-ML to share data with other libraries without creating copies of data. This paper describes the design, implementation, and evaluation of SHMEM-ML – demonstrating a general purpose system for data transformation and manipulation while achieving up to a 38× speedup in distributed training performance relative to the industry standard Horovod framework without a regression in model metrics. 

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3. An Object Storage for Distributed Acoustic Sensing

   https://doi.org/10.1785/0220230172  

   Ni, Yiyu; Denolle, Marine A; Fatland, Rob; Alterman, Naomi; Lipovsky, Bradley P; Knuth, Friedrich (October 2023, Seismological Research Letters)

   Abstract Large-scale processing and dissemination of distributed acoustic sensing (DAS) data are among the greatest computational challenges and opportunities of seismological research today. Current data formats and computing infrastructure are not well-adapted or user-friendly for large-scale processing. We propose an innovative, cloud-native solution for DAS seismology using the MinIO open-source object storage framework. We develop data schema for cloud-optimized data formats—Zarr and TileDB, which we deploy on a local object storage service compatible with the Amazon Web Services (AWS) storage system. We benchmark reading and writing performance for various data schema using canonical use cases in seismology. We test our framework on a local server and AWS. We find much-improved performance in compute time and memory throughout when using TileDB and Zarr compared to the conventional HDF5 data format. We demonstrate the platform with a computing heavy use case in seismology: ambient noise seismology of DAS data. We process one month of data, pairing all 2089 channels within 24 hr using AWS Batch autoscaling. 

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4. Scaling databases and file apis with programmable ceph object storage

   LeFevre, Jeff Maltzahn (February 2020, USENIX VAULT'20)

   The Skyhook Data Management project (SkyhookDM.com) at the Center for Research in Open Source Software (cross.ucsc.edu) at UC Santa Cruz implements customized extensions through Ceph's object class interface that enables offloading database operations to the storage system. In our previous Vault '19 talk, we showed how SkyhookDM can transparently scale out databases. The SkyhookDM Ceph extensions are an example of our 'programmable storage' research efforts at UCSC, and can be accessed through commonly available external/foreign table database interfaces. Utilizing fast in-memory serialization libraries such as Google Flatbuffers and Apache Arrow, SkyhookDM currently implements common database functions such as SELECT, PROJECT, AGGREGATE, and indexing inside Ceph, along with lower-level data manipulations such as transforming data from row to column formats on RADOS servers. In this talk, we will present three of our latest developments on the SkyhookDM project since Vault '19. First, SkyhookDM can be used to also offload operations of access libraries that support plugins for backends, such as HDF5 and its Virtual Object Layer. Second, in addition to row-oriented data format using Google's Flatbuffers, we have added support for column-oriented data formats using the Apache Arrow library within our Ceph extensions. Third, we added dynamic switching between row and column data formats within Ceph objects, a first step towards physical design management in storage systems, similar to physical design tuning in database systems. 

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5. HAL: Computer System for Scalable Deep Learning

   https://doi.org/10.1145/3311790.3396649  

   Kindratenko, Volodymyr; Mu, Dawei; Zhan, Yan; Maloney, John; Hashemi, Sayed Hadi; Rabe, Benjamin; Xu, Ke; Campbell, Roy; Peng, Jian; Gropp, William (January 2020, PEARC '20: Practice and Experience in Advanced Research Computing)

   We describe the design, deployment and operation of a computer system built to efficiently run deep learning frameworks. The system consists of 16 IBM POWER9 servers with 4 NVIDIA V100 GPUs each, interconnected with Mellanox EDR InfiniBand fabric, and a DDN all-flash storage array. The system is tailored towards efficient execution of the IBM Watson Machine Learning enterprise software stack that combines popular open-source deep learning frameworks. We build a custom management software stack to enable an efficient use of the system by a diverse community of users and provide guides and recipes for running deep learning workloads at scale utilizing all available GPUs. We demonstrate scaling of a PyTorch and TensorFlow based deep neural networks to produce state-of-the-art performance results. 

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