DARPA seeks to create new networking approaches to accelerate distributed application performance by 100x

#Announcement(Startup) #Invitation #Awards [ via IoTGroup ]
#DueDateTime : 8 Oct 2019 #StartDateTime : 7 Nov 2019 #Organizer : DARPA

Part of FastNICs will focus on developing hardware systems to significantly improve aggregate raw server datapath speed. Within this research area, researchers will design, implement, and demonstrate 10 Tbps network interface hardware using existing or road-mapped hardware interfaces. The hardware solutions must attach to servers via one or more industry-standard interface points, such as I/O buses, multiprocessor interconnection networks, and memory slots, to support the rapid transition of FastNICs technology. “It starts with the hardware; if you cannot get that right, you are stuck. Software can’t make things faster than the physical layer will allow so we have to first change the physical layer,” said Smith.

A second research area will focus on developing system software required to manage the FastNICs hardware resources. To realize 100x throughput gains at the application level, system software must enable efficient and parallel transfer of data between the network hardware and other elements of the system. FastNICs researchers will work to generate software libraries – all of which will be open source, and compatible with at least one open source OS – that are usable by various applications.

FastNICs will also explore applications that could be enabled by the multiple order of magnitude performance increases provided by the program-generated hardware. Researchers will aim to design and implement at least one application that demonstrates a 100x speedup when executed on the novel hardware/software stack, providing a validator for the program’s primary objective. There are two application areas of particular interest – distributed machine learning and sensors. Machine learning requires the harnessing of clusters – or large numbers of machines – so that all cores are employed for a single purpose, like analyzing imagery to help self-driving cars appropriately identify an obstacle in the road. “Recent research has shown that by speeding up the network support, the entire distributed machine learning system can operate more quickly. With machine learning, the methods typically used involve moving data around, which creates delays. However, if you can move data more quickly between machines with a successful FastNICs result then you should be able to shrink the performance gap,” said Smith.

FastNICs will also explore sensor data from systems like UAVs and overhead imagers. An example application would be change detection where tagged images are used to train a deep learning system to recognize anomalies in a time series of image captures, such as the presence of a strange structure, or a sudden spurt in activity at facilities in an inexplicable location. Change detection requires quick access to both current sensor data as well as the ability to rapidly access archives of data.

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