Dataplane Specialization for High-performance OpenFlow Software Switching

OpenFlow is an amazingly expressive dataplane program- ming language, but this expressiveness comes at a severe performance price as switches must do excessive packet clas- sification in the fast path. The prevalent OpenFlow software switch architecture is therefore built on flow caching, but this imposes intricate limitations on the workloads that can be supported efficiently and may even open the door to mali- cious cache overflow attacks. In this paper we argue that in- stead of enforcing the same universal flow cache semantics to all OpenFlow applications and optimize for the common case, a switch should rather automatically specialize its dat- aplane piecemeal with respect to the configured workload. We introduce ES WITCH , a novel switch architecture that uses on-the-fly template-based code generation to compile any OpenFlow pipeline into efficient machine code, which can then be readily used as fast path. We present a proof- of-concept prototype and we demonstrate on illustrative use cases that ES WITCH yields a simpler architecture, superior packet processing speed, improved latency and CPU scala- bility, and predictable performance. Our prototype can eas- ily scale beyond 100 Gbps on a single Intel blade even with complex OpenFlow pipelines

Large-Scale Optical Neural Networks based on Photoelectric Multiplication

Recent success in deep neural networks has generated strong interest in hardware accelerators to improve speed and energy consumption. This paper presents a new type of photonic accelerator based on coherent detection that is scalable to large ($N \gtrsim 10^6$) networks and can be operated at high (GHz) speeds and very low (sub-aJ) energies per multiply-and-accumulate (MAC), using the massive spatial multiplexing enabled by standard free-space optical components. In contrast to previous approaches, both weights and inputs are optically encoded so that the network can be reprogrammed and trained on the fly. Simulations of the network using models for digit- and image-classification reveal a "standard quantum limit" for optical neural networks, set by photodetector shot noise. This bound, which can be as low as 50 zJ/MAC, suggests performance below the thermodynamic (Landauer) limit for digital irreversible computation is theoretically possible in this device. The proposed accelerator can implement both fully-connected and convolutional networks. We also present a scheme for back-propagation and training that can be performed in the same hardware. This architecture will enable a new class of ultra-low-energy processors for deep learning.Comment: Text: 10 pages, 5 figures, 1 table. Supplementary: 8 pages, 5, figures, 2 table

Guaranteed bandwidth implementation of message passing interface on workstation clusters

Due to their wide availability, networks of workstations (NOW) are an attractive platform for parallel processing. Parallel programming environments such as Parallel Virtual Machine (PVM), and Message Passing Interface (MPI) offer the user a convenient way to express parallel computing and communication for a network of workstations. Currently, a number of MPI implementations are available that offer low (average ) latency and high bandwidth environments to users by utilizing an efficient MPI library specification and high speed networks. In addition to high bandwidth and low average latency requirements, mission critical distributed applications, audio/video communications require a completely different type of service, guaranteed bandwidth and worst case delays (worst case latency) to be guaranteed by underlying protocol. The hypothesis presented in this paper is that it is possible to provide an application a low level reliable transport protocol with performance and guaranteed bandwidth as close to the hardware on which it is executing. The hypothesis is proven by designing and implementing a reliable high performance message passing protocol interface which also provides the guaranteed bandwidth to MPI and to mission critical distributed MPI applications. This protocol interface works with the Fiber Distributed Data Interface (FDDI) driver which has been designed and implemented for Performance Technology Inc. commercial high performance FDDI product, the Station Management Software 7.3, and the ADI / MPICH (Argonne National Laboratory and Mississippi State University\u27s free MPI implementation)