Cloud Computing

With data centers in the cloud, search is evolving rapidly. New cloud-based architectures and rapidly changing workload require the ability to develop flexible, scalable applications. FPGAs allow designers to optimize and refine search algorithms continuously. This acceleration benefits functionality including two rising trends:

• “Verticalization” targeting cloud services to vertical markets
• Disaggregated data in which data is collected from multiple sources, compiled, analyzed, and broken out into segments

FPGAs excel in search because their flexibility allows easy implement of features such as hashes and lookup tables. For example, Arria® 10 hard floating-point operations are vital to this acceleration.

FPGAs are flexible, multi-purpose, reconfigurable acceleration devices. They can perform a variety of functions in the data center such as search, intelligent NIC functions, machine learning scoring acceleration with Xeon processors for best performance per watt, encryption, compression, and analytics. Large data center operators prefer to deploy common hardware to use one server configuration for flexible workload deployment. Including FPGAs in the configuration gives data center operators the option to adjust their server functions without changing the hardware.

Data streams, such as computer network traffic, data feeds, sensor data, and event stream processing (ESP) involve large, complex data sets. To process this data, several new applications have evolved. For example, the open-source Hadoop framework can process data faster by utilizing thousands of systems simultaneously. Another open-source framework, Spark, provides a Hadoop-compatible, data processing engine. These tools, however, are processor intensive and the CPU can become a bottleneck. Using FPGAs, data centers can implement these specific applications at a hardware level significantly increasing the system's performance per watt.

Data streams, such as computer network traffic, data feeds, sensor data, and event stream processing (ESP) involve large, complex data sets. To process this data, several new applications have evolved. For example, the open-source Hadoop framework can process data faster by utilizing thousands of systems simultaneously. Another open-source framework, Spark, provides a Hadoop-compatible, data processing engine. These tools, however, are processor intensive and the CPU can become a bottleneck. Using FPGAs, data centers can implement these specific applications at a hardware level significantly increasing the system's performance per watt.

Today's CPUs are evolving to contain more and more cores, but the bandwidth to external memory is not growing at the same pace of as this multi-core computing power. FPGAs can relieve the CPU data access bottlenecks by providing compression, filtering, and de-duplication functions. FPGAs can also compress data more efficiently, for example in Hadoop where they accelerate the “shuffle” phase, when the results are brought back to a single server. Advantages are also seen with Spark, where the FPGA can accelerate data streaming, real and predictive data, shuffle phase compression, and other functions.

With FPGAs, users can perform real-time analytics, such as predicting the class or value of new data instances, and can also filter data for storage. In these applications, using one or more FPGAs efficiently accelerates complex data processing.