Robert R. McCormick School of Engineering and Applied Science Electrical Engineering and Computer Science Department Center for Ultra-scale Computing and Information Security at Northwestern University


This project is sponsored by DOE's Office of Science as part of the FAST-OS program.
The award number is DE-FG02-08ER25848/A002.

Project Team Members:

Northwestern University

Argonne National Laboratory

Northwestern University - EECS Dept.

Active Storage with Analytics Capabilities and I/O Runtime System for Petascale Systems


Many of the important computational applications in science and engineering, sensor processing, and other disciplines have grown in complexity, scale, and the data set sizes that they produce, manipulate, and consume. Most of these applications have a data intensive component. For example, in applications such as climate modeling, combustion, and astrophysics simulations, data set sizes range between 100 TB and 10 PB, and the required compute performance is 100+ teraops. In other areas, data volumes grow as a result of aggregation. For example, proteomics and genomics applications may take several thousand 10-megabyte mass spectra of cell contents at thousands of time points and conditions, yielding a data rate of terabytes per day. Scientists and engineers require techniques, tools, and infrastructure to better understand this huge amount of data, in particular to effectively perform complex data analysis, statistical analysis, and knowledge discovery. As an example, scientists have been able to scale simulations to more than tens of thousands of processors (and for more than a million CPU-hours), but efficient I/O has been a problem. Indeed, in some cases, once the simulation is finished in a couple of days, several months are needed to organize, analyze, and understand the data. From a productivity perspective, this is a staggering number.

The performance of data analysis tasks that heavily rely on parallel file systems to perform their I/O is typically poor, mainly because of the cost of data transfer between the nodes that store the data and the nodes that perform the analysis. For applications that filter a huge amount of input data, the idea of an active storage system has been proposed to reduce the bandwidth requirement by moving the computation closer to the storage nodes. The concept of active storage is well suited for data-intensive applications; however, several limitations remain, especially in the context of parallel file systems. First, scientists and engineers use a variety of data analysis kernels including simple statistical operations, string pattern matching, visualization, and data mining kernels. Current parallel file systems lack a proper interface to utilize these various analysis kernels embedded in the storage side, thereby preventing wide deployment of active storage systems. Second, files in parallel file systems are typically striped across multiple servers and are often not perfectly aligned with respect to computational unit, making it difficult to process data locally in the general case. Third, most analysis tasks need to be able to broadcast and/or reduce the locally (partially) read or computed data with the data from other nodes. Unfortunately, current parallel file systems lack server-side communication primitives for aggregation and reduction.

The main contribution of this paper is an active storage system on parallel I/O software stacks. Our active storage system enables data analytic tasks within the context of parallel file systems through three key features:

  • Enhanced runtime interface that uses predefined kernels in parallel file systems: We expose the semantics of predefined analysis kernels, such as the data type of data blocks on the disk, to parallel file systems so that execution of embedded kernels is possible on the server.
  • Stripe alignment during runtime: In order to allow a file server to perform proper computation on striped files, our system adjusts to misaligned computational units by pulling missing bytes, when needed, from the neighboring servers that hold them.
  • Server-to-server communication for aggregation and reduction: In order to perform computation entirely on the server side, servers need to communicate their partial (local) results with other servers to obtain the complete results. To this end, we augmented the storage servers with basic collective communication primitives (e.g., broadcast and allreduce).

  • We have implemented a prototype of our active storage system and demonstrate its benefits using four data analysis benchmarks. Our experimental results show that our proposed system improves the overall performance of all four benchmarks by 50.9% on average and that the compute-intensive portion of the k-means clustering kernel can be improved by 58.4% through GPU offloading when executed with a larger computational load. We also show that our scheme consistently outperforms the traditional storage model with a wide variety of input dataset sizes, number of nodes, and computational loads.

    Figure 1. Two models for performing data analysis: (a) traditional storage model, (b) active storage model. In (a), high-level data information, such as data structures and attributes types, are lost when data reaches lower layers, typically converted to just (striped) byte streams in parallel file systems.


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