FLICK: developing and running application-specific network services

Data centre networks are increasingly programmable, with application-specific network services proliferating, from custom load-balancers to middleboxes providing caching and aggregation. Developers must currently implement these services using traditional low-level APIs, which neither support natural operations on application data nor provide efficient performance isolation. We describe FLICK, a framework for the programming and execution of application-specific network services on multi-core CPUs. Developers write network services in the FLICK language, which offers high-level processing constructs and application-relevant data types. FLICK programs are translated automatically to efficient, parallel task graphs, implemented in C++ on top of a user-space TCP stack. Task graphs have bounded resource usage at runtime, which means that the graphs of multiple services can execute concurrently without interference using cooperative scheduling. We evaluate FLICK with several services (an HTTP load-balancer, a Memcached router and a Hadoop data aggregator), showing that it achieves good performance while reducing development effort

Profile Guided Dataflow Transformation for FPGAs and CPUs

This paper proposes a new high-level approach for optimising field programmable gate array (FPGA) designs. FPGA designs are commonly implemented in low-level hardware description languages (HDLs), which lack the abstractions necessary for identifying opportunities for significant performance improvements. Using a computer vision case study, we show that modelling computation with dataflow abstractions enables substantial restructuring of FPGA designs before lowering to the HDL level, and also improve CPU performance. Using the CPU transformations, runtime is reduced by 43 %. Using the FPGA transformations, clock frequency is increased from 67MHz to 110MHz. Our results outperform commercial low-level HDL optimisations, showcasing dataflow program abstraction as an amenable computation model for highly effective FPGA optimisation

Guide to Good Practice in using Open Source Compilers with the AGCC Lexical Analyzer

Quality software always demands a compromise between users' needs and hardware resources. To be faster means expensive devices like powerful processors and virtually unlimited amounts of RAM memory. Or you just need reengineering of the code in terms of adapting that piece of software to the client's hardware architecture. This is the purpose of optimizing code in order to get the utmost software performance from a program in certain given conditions. There are tools for designing and writing the code but the ultimate tool for optimizing remains the modest compiler, this often neglected software jewel the result of hundreds working hours by the best specialists in the world. Even though, only two compilers fulfill the needs of professional developers, a proprietary solution from a giant in the IT industry, and the Open source GNU compiler, for which we develop the AGCC lexical analyzer that helps producing even more efficient software applications. It relies on the most popular hacks and tricks used by professionals and discovered by the author who are proud to present them further below.registers, dynamic linkage, cache, null pointers, tweaking