Introduction

In most industries today, (whether it is financial services, manufacturing, academic research, healthcare and life sciences, or energy exploration) data analysis, modeling, and visualization efforts are critical to success.

To gain a competitive edge, most organizations are incorporating ever-large data sets and more variable data formats into these computational workflows to help derive better information upon which to make smarter decisions.

These big data applications are placing new attention on the high performance computing (HPC) solutions used to run the algorithms and process the raw data. Due to the larger volumes and greater variety of data types, as well as the desire to use more robust analysis, modeling, and visualization routines, HPC solutions can be used to provide high sustained I/O and throughput, while being optimized to cost-effectively handle highly variable workflows.

The essential element in all of this work is a need for speed. Organizations need fast time-to-results so that they can make the right decisions (which well to drill, which new drug candidate to develop, which product design to produce, which customer to award a lower rate loan to) before their competitors.

Complications and challenges that can impede HPC workflows

When looking to accelerate HPC workloads, there are several factors that can play a major role in overall performance.

To start, today’s analysis, modeling, and visualization efforts are carried out using much more sophisticated algorithms in order to derive more detailed and realistic results. The output from these routines offers finer spatial or temporal resolution and consequently results in much larger size output data sets. In a typical workflow, those output files might be used as input to another analysis, modeling, or visualization application.

These operations can impact HPC workflows since the great volumes of data produced by the initial run must be written to disk and saved and then the data must be ingested by yet another routine. Both operations can generate high I/O and throughput demands on an infrastructure. And if the infrastructure is not capable of sustaining these data transfers, the computational workflows can slow significantly.

Another factor has to do with the data that is being used in today’s analysis, modeling, and visualization efforts. Nearly every industry is now making use of much larger data sets, richer sets (such as that produced from newer seismic imaging tools or next-generation sequencers), and many more types of data. However, most users, even those who primarily have large data sets, also have large numbers of small files – even if they consume a relatively small percentage of the total capacity.

Big data and HPC solutions must therefore not only be capable of quickly accessing the large volumes of data required for the computations, they also must intelligently stage the different types of data, which comes in varying file formats and sizes, on suitably high performance storage.

Required storage solution characteristics

Organizations continually deploy new servers with more powerful CPUs to improve and speed up their analysis, modeling, and visualization efforts. To make the best use of such computing resources, an HPC solution must have a suitable storage solution to sustain HPC workflows.

A storage solution for today’s big data and HPC environments must be able to easily scale. Some solutions offer help meeting the growing data volume demands, but fall short when trying to keep CPUs satiated. To help accelerate HPC workflows, a storage solution must also scale in performance so that as the data volumes grow, the system supports the higher I/O and throughput required to get faster results.

Finally, a storage solution must be optimized to handle today’s HPC big data workflows consisting of data sets of files of all sizes. If all data used were in the same format – a structured database, for example – or of the same relative file size, a solution could be highly optimized to handle the specific data. Working with the mixed data sets used today requires a storage solution that optimizes workflow performance for each data type.

Panasas introduces an integrated SSD/SATA approach

Panasas ActiveStor storage systems have a modular blade architecture integrated with its PanFS parallel file system. The design eliminates the bottleneck of a single RAID controller to deliver high-performance, scalable storage. Prior generations of ActiveStor have been based solely on SATA drives and were well-tuned for high throughput.

With the fifth-generation ActiveStor 14, Panasas has taken a unique approach, leveraging lightning fast SSDs integrated with high capacity SATA disk to improve storage performance while keeping costs down. Rather than use SSD for caching or for “most recent” file access as many other vendors have done, ActiveStor 14 stores all metadata and small files (less than 60KB) on the SSDs and larger files on SATA drives.

Metadata is accessed frequently so fast metadata access benefits all types of workloads. All file operations, including reads and writes, require access to metadata. In many cases, such as directory listings, access to the metadata is all that is required to satisfy an I/O request. Storing metadata on SSD boosts performance for all storage operations, especially for directory functions (listing, searches, etc.) and RAID rebuilds in the event of a drive error. Rebuild performance has been improved so that the new 4TB drives can be rebuilt in the same amount of time as the 3TB drives in the prior generation ActiveStor 12, maintaining a high level of data integrity and system reliability.

Small file access can be disproportionately slow when read from, or written to, standard hard disk drives. Accesses of less than a full sector are inefficient, particularly for random I/O. Furthermore, reads and writes of small files can conflict with streaming reads or writes of large files to the same disk. By maintaining small files on SSD, such conflicts are eliminated. In addition, ActiveStor 14 stores the first 12KB of all files inside the file system metadata, improving SSD efficiency while increasing small file performance. This efficient storage of small files on SSD, dramatically improves response time and IOPS, as evidenced by very impressive SPEC sfs2008 NFS IOPS results that Panasas has published.

ActiveStor 14 is available in three configurations with varying sizes of SSD, SATA and cache. The amount of SSD for acceleration ranges from 1.5 percent up to 10.7 percent of total storage capacity. The bulk of the storage capacity, however, is on cost-effective SATA drives, keeping the overall cost per terabyte lower than the prior generation, and very competitive in the market today.

The Importance of Ease of Use and Management

Equally important to the performance and reliability of any storage system is the ease of use and management of the product. With ActiveStor, organizations can simply add blade enclosures to non-disruptively increase capacity and performance of the global file system as storage requirements grow. Parallel access to data and automated load balancing ensure that performance is optimized. This makes it easy to linearly scale capacity to over eight petabytes and performance to 150GB/s or 1.4M IOPS.

Conclusion

The end result is a high-performance storage system that delivers high throughput and IOPS, ideal for the most demanding HPC and big data workloads and accelerates time-to-results. ActiveStor delivers unmatched scale-out NAS performance in addition to the manageability, reliability, and value required by demanding computing organizations in the biosciences, energy, finance, government, manufacturing, media, and other research sectors.

To learn more about how the Panasas ActiveStor 14 can help your organization, register for the live webinar: http://www.panasas.com/news/webinars