by George Lawton

AWS’ FPGA and Elastic GPU instances both appeal to customers with high-performance computing workloads, but admins should note these important differences between the two.

AWS ran the numbers and decided to come out with several new accelerated computing instance types that promise better performance for compute-intensive applications.

Elastic graphics processing units (GPUs) optimize performance for apps that manage workloads such as data analytics, machine learning and deep learning. Field-programmable gate array (FPGA) instances also provide better performance for well-defined tasks and supported algorithms but are less flexible than Elastic GPU instances.

Both instance types, which feature dedicated math coprocessors, are in their infancies. They also both work in concert with CPUs for accelerated computing within math-intensive apps.

Elastic GPUs, however, have a more mature application development ecosystem, which enables developers to more easily change or adapt GPU-based applications. For that reason, Elastic GPUs are likely a better fit for workloads such as exploratory analytics, as they make it easier to dynamically update and test different algorithms. With GPUs, developers merely run different software to implement these approaches. FPGAs, in contrast, need more tuning to test different algorithms. For these reasons, GPUs are also a better fit for data visualization, 3D modeling and simulation.

FPGAs promise better performance per unit and reduced power consumption than CPUs alone or CPUs combined with GPUs. While it’s not clear how power savings translate into cost savings, many AWS FPGA applications run considerably faster. For example, Ryft, a big data analytics platform company, said it runs search processes faster, and its 1 TB log file processes 91 times faster than running on a CPU alone.

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