Arm processors have been explored in HPC for several years, however there has not yet been a demonstration of viability for supporting large-scale production workloads. In this paper, we offer a retrospective on the process of bringing up Astra, the first Petascale supercomputer based on 64-bit Arm processors, and validating its ability to run production HPC applications. Through this process several immature technology gaps were addressed, including software stack enablement, Linux bugs at scale, thermal management issues, power management capabilities, and advanced container support. From this experience, several lessons learned are formulated that contributed to the successful deployment of Astra. These insights can be helpful to accelerate deploying and maturing other first-seen HPC technologies. With Astra now supporting many users running a diverse set of production applications at multi-thousand node scales, we believe this constitutes strong supporting evidence that Arm is a viable technology for even the largest-scale supercomputer deployments.

Predicting access times is a crucial part of predicting hard disk drive performance. Existing approaches use white-box modeling and require intimate knowledge of the internal layout of the drive, which can take months to extract. Automatically learning this behavior is a much more desirable approach, requiring less expert knowledge, fewer assumptions, and less time. While previous research has created black-box models of hard disk drive performance, none have shown low per-request errors. A barrier to machine learning of access times has been the existence of periodic behavior with high, unknown frequencies. We identify these high frequencies with Fourier analysis and include them explicitly as input to the model. In this paper we focus on the simulation of access times for random read workloads within a single zone. We are able to automatically generate and tune request-level access time models with mean absolute error less than 0.15 ms. To our knowledge this is the first time such a fidelity has been achieved with modern disk drives using machine learning. We are confident that our approach forms the core for automatic generation of access time models that include other workloads and span across entire disk drives, but more work remains.

In this article, we present LWDLS, a lightweight data location service designed for Exascale storage systems (storage systems with order of 10 18 bytes) and geo-distributed storage systems (large storage systems with physically distributed locations). LWDLS provides a search-based data location solution, and enables free data placement, movement, and replication. In LWDLS, probe and prune protocols are introduced that reduce topology mismatch, and a heuristic flooding search algorithm (HFS) is presented that achieves higher search efficiency than pure flooding search while having comparable search speed and coverage to the pure flooding search. LWDLS is lightweight and scalable in terms of incorporating low overhead, high search efficiency, no global state, and avoiding periodic messages. LWDLS is fully distributed and can be used in nondeterministic storage systems and in deterministic storage systems to deal with cases where search is needed. Extensive simulations modeling large-scale High Performance Computing (HPC) storage environments provide representative performance outcomes. Performance is evaluated by metrics including search scope, search efficiency, and average neighbor distance. Results show that LWDLS is able to locate data efficiently with low cost of state maintenance in arbitrary network environments. Through these simulations, we demonstrate the effectiveness of protocols and search algorithm of LWDLS.

Many storage systems include computationally expensive components. Examples include encryption for confidentiality, checksums for integrity, and error correcting codes for reliability. As storage systems become larger, faster, and serve more clients, the demands placed on their computational components increase and they can become performance bottlenecks. Many of these computational tasks are inherently parallel: they can be run independently for different blocks, files, or I/O requests. This makes them a good fit for GPUs, a class of processor designed specifically for high degrees of parallelism: consumer-grade GPUs have hundreds of cores and are capable of running hundreds of thousands of concurrent threads. However, because the software frameworks built for GPUs have been designed primarily for the long-running, data-intensive workloads seen in graphics or high-performance computing, they are not well-suited to the needs of storage systems.

SUMMARY Reed–Solomon coding is a method for generating arbitrary amounts of erasure correction information from original data via matrix–vector multiplication in finite fields. Previous work has shown that modern CPUs are not well‐matched to this type of computation, requiring applications that depend on Reed–Solomon coding at high speeds (such as high‐performance storage arrays) to use hardware implementations. This work demonstrates that high performance is possible with current cost‐effective graphics processing units across a wide range of operating conditions and describes how performance will likely evolve in similar architectures. It describes the characteristics of the graphics processing unit architecture that enable high‐speed Reed–Solomon coding. A high‐performance practical library, Gibraltar, has been prototyped that performs Reed–Solomon coding on graphics processors in a manner suitable for storage arrays, along with applications with similar data resiliency needs. This library enables variably resilient erasure correcting codes to be used in a broad range of applications. Its performance is compared with that of a widely available CPU implementation, and a rationale for its API is presented. Its practicality is demonstrated through a usage example. Copyright © 2011 John Wiley & Sons, Ltd.

While RAID is the prevailing method of creating reliable secondary storage infrastructure, many users desire more flexibility than offered by current implementations. Traditionally, RAID capabilities have been implemented largely in hardware in order to achieve the best performance possible, but hardware RAID has rigid designs that are costly to change. Software implementations are much more flexible, but software RAID has historically been viewed as much less capable of high throughput than hardware RAID controllers. This work presents a system, Gibraltar RAID, that attains high RAID performance by offloading the calculations related to error correcting codes to GPUs. This paper describes the architecture, performance, and qualities of the system. A comparison to a well-known software RAID implementation, the md driver included with the Linux operating system, is presented. While this work is presented in the context of high performance computing, these findings also apply to a general RAID market.

Graphical Processing Units (GPUs) have been applied to more types of computations than just graphics processing for several years. Until recently, however, GPU hardware has not been capable of efficiently performing general data processing tasks. With the advent of more general-purpose extensions to GPUs, many more types of computations are now possible. One such computation that we have identified as being suitable for the GPU’s unique architecture is Reed-Solomon coding in a manner appropriate for RAID-type systems. In this paper, we motivate the need for RAID with triple-disk parity and describe a pipelined architecture for using a GPU for this purpose. Performance results show that the GPU can outperform a modern CPU on this problem by an order of magnitude and also confirm that a GPU can be used to support a system with at least three parity disks with no performance penalty.