Every organization wants to be all-in on digital transformation, seeking to dive deep into its big data platforms to drive efficiency and optimization. As these efforts become more critical to your enterprise, the priority becomes building an infrastructure that can meet the demand for a much higher computational power. CPU clustering has been one avenue, but Moore’s law is no longer applicable.

Cluster scaling has inherent diminishing returns, leaving many unprepared, with the seemingly infinite growth of processing power now gone. It fueled technology innovation for more than 50 years. Stakeholders need a new path. Most businesses may instinctively think that buying more servers will give them what they need, but that’s a proposition that requires a substantial investment.

For these reasons, the industry has realized that the time for using general purpose processors for demanding applications is no longer viable. The path forward is to leverage a new generation of hardware solutions that are good at running these demanding applications at a much faster speed. GPUs have been the first one out of the gate to accomplish this for AI applications. FPGAs have tremendous versatility in the sense that they have the flexibility of general purpose processors, but with the execution speed of a specialized hardware for a specific data pipeline.

Manufacturers such as Samsung and Xilinx have developed FPGA-based solutions such as SmartSSD and Alveo, respectively, that help you achieve greater performance. It would seem that this could be the answer to hardware acceleration. In theory, all that's needed is to purchase the specialized hardware and program it. However, it’s not that easy, leaving most enterprises unable to use this innovation to boost their performance.

With advanced hardware readily available, what’s the obstacle stopping businesses from putting them into action?


What’s So Hard About Hardware Acceleration?

The challenge that occurs when most companies want to use advanced hardware comes down to a skill set gap. Data scientists are well trained in their specialty, but programming this advanced hardware requires a different set of abilities. Some organizations are able to bring in hardware engineers to execute this, but it’s costly, and those with this proficiency are hard to find. Without custom programming, you simply cannot achieve acceleration and improve the performance of your big data analytics platform.


Not Bridging the Gap: What Do You Risk?

To accelerate your time-to-insight as a competitive advantage in your industry, you need to build that bridge. Legacy systems and disjointed data streams won’t deliver the business intelligence you need to be nimble. Being proactive in managing big data is vital to the longevity of your company.

Without bridging the gap, you risk falling behind others because your hardware can’t keep up. You see insights late and can’t capitalize on them. Your peers may already be using new technology layers to achieve acceleration. They could be three or four steps ahead of you.

What if the gap didn’t exist? In a perfect world, you would eliminate the gap and be able to act in the moment based on your data.

Better Decisions in the Moment 

Let’s think about how financial institutions use big data to mitigate fraud. They use AI and machine learning algorithms to look for anomalies. Their goal is to prevent the breach, shut down the credit card, and notify the cardholder immediately.

Financial institutions want to get better at fraud prevention because fraud eats away at revenue. A report from LexisNexis found that fraud increased by 9.3 percent for financial institutions from 2017 to 2018. Institutions are seeking ways to reduce these losses. They want to yield higher performance but may be unsure of how to optimize their hardware.


Closing the Gap: Technology Tools That Allow You to Take on Big Data Workloads Faster

If custom programming by engineers isn’t viable for your company, you may think it’s impossible to attain hardware acceleration. It’s not. We recognized this gap in the field and developed an innovative new approach that doesn’t require custom coding. 


Hyperacceleration brings innovation to address the programming model gap. Hyperacceleration bridges the gap between existing analytics applications implemented to run on Spark, and the hardware accelerators installed in the system. The most important aspect of Hyperacceleration is that it does its magic completely transparent to the users. That is what we call zero code change. So why does this matter, and how does it address the weaknesses of previous hardware acceleration attempts?

Hyperacceleration leverages hardware based accelerator templates that program the FPGA on the fly to accelerate various operations of the user's program. We particularly focused on the FPGA accelerator as our first hardware platform since it has tremendous flexibility, high performance, and very low power consumption.

Our solution also requires no code changes on big data platforms such as Spark. It enables automation of acceleration by inserting a data flow adaptation layer. Then it actuates FPGA acceleration via bitfile templates, boosting your performance up to 10x. It’s a cost-effective way to speed up your big data platforms without compromising or disrupting your workflows.


Realize True and Successful Hardware Acceleration

Achieving hardware acceleration has been challenging, requiring work-arounds and extensive resources. Now, a simpler, accessible solution is available.

You can further explore hyperacceleration to understand how to use it with your big data initiatives by downloading our white paper, Hyperacceleration with Bigstream Technology. Get all the facts and details and achieve the high-performance competitive edge for your organization.