Watch Out For the Top Five Throughput Killers

Written by Ouafae Hannaoui

Written by Ouafae Hannaoui

Published on January 26, 2020

Data throughput is the bread and butter of modern applications. It’s what keeps user experiences humming, keeps workloads moving, and keeps business processes and revenues flowing. In high-performance computing (HPC) environments in particular—whether for scientific research, healthcare, media, finance—higher throughput invariably translates to better business outcomes.

Unfortunately, achieving great throughput is not always easy, even when you’re using ultra-fast compute and network fabric. That’s because inside even the most advanced HPC environments, there are all sorts of hidden bottlenecks that eat away at the performance your system can achieve.

Here are the top five throughput killers:

  1. Overworked central processing unit (CPU): Like a short-order cook during the lunchtime rush, throw too many requests at your CPU at once, and you end up with a backlog (and surly customers). In computing, this translates to a queue of unmet application requests, which adds even more stress to the CPU, creating a vicious cycle of diminishing performance. Faulty memory can be a culprit here, but more often, it’s an overly engaged I/O device hogging all the CPU’s attention.
  1. Inefficient long-term storage: When your application uses random-access memory (RAM), it can zip right along. But the minute RAM reaches capacity and the system has to transfer data to long-term storage, everything slows down—especially if that storage is a legacy hard disk drive (HDD). The reverse is even worse; to retrieve data from an old-school HDD, the drive head has to scan the entire disk, locating and reassembling all the fragments of the requested data.
  1. Memory limitations: In some (especially older) systems, short-term RAM isn’t efficient enough to keep up with the CPU, which then has to sit around waiting for data. Even in modern systems, the basic way that systems use memory (pulling data from long-term storage, swapping out current data in RAM) inevitably creates overhead.
  1. Network overwhelm: It’s possible for the network communication device that mediates data transmission to your system to get buried in a flood of data with which it can’t keep up. Similarly, if your server runs out of resources such as hard drive space, the CPU won’t be able to keep pace.
  1. Software limitations: In some cases, bottlenecks can be caused by your application, not the hardware. Some software is hardcoded with predetermined limitations on the number of processing tasks it can sustain—regardless of your device’s processing capability or RAM.

Boost Your Throughput

If you’re not getting the data throughput your applications need, there are steps you can take to improve it, such as increasing or replacing RAM, adding more storage, or increasing capacity. Organizations running HPC environments, however, have often already maxed out those options. At that point, you need to consider novel approaches to the problem.

Focusing on long-term storage is a great place to start because there’s a dirty little secret at the heart of the storage world: most architectures still rely on software and file systems designed for decades-old HDD technology. Even if you’re using modern flash media and the fastest storage interconnects, your throughput gets throttled by processes that date back to the days of disco, that still function like they’re working with physical spinning disks.

The good news is that these legacy software limitations are most definitely a problem you can do something about. To see how Stellus is helping organizations break the storage bottleneck and reach new levels of data throughput, visit our product’s page to try Stellus Data Platform for yourself, and then tell us what you think.

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