CoDeR-MP SSF meeting, May 3, 2011, Uppsala Agenda  Overview (Coffee will be served) Introduction, Olof Lindgren CoDeR-MP: Goals, progress and vision, Wang Yi Discussion  Hard safety-critical real-time applications CoDeR-MP solved the 37-year open problem! Guan Nan Coloring the cache to isolate multiple applications, Wang Yi  Break  Soft high-performance real-time applications Performance profiling and modeling, David Eklöv & Erik Hagersten The multi-core locking problem, Pan Xiaoyue & Bengt Jonsson  Industrial applications: real-time signal processing Real-time model-based estimation, Alexander Medvedev, UU SAAB’s perspective on multi-core, Mats Ekman & Björn Holmberg, SAAB  Lunch & Discussion

CoDeR-MP Computationally Demanding Real-Time Applications on Multicore Platforms OUTLINE Why CoDeR-MP Project Plan Structure Goals Progress Main achievements Demos Vision

The free lunch is over & Multicores are coming ! Year

CPU L1 CPU L1 CPU L1 CPU L1 CPU L1 CPU L1 CPU L1 CPU L1 Typical Multicore Architecture L2 Cache Off-chip memory 5

Theoretically with multicore, you may get:  Higher Performance Increasing the cores -- unlimited computing power  !  Lower Power Consumption Increasing the cores, decreasing the clock frequency  Keep the “same performance” using ¼ of the energy 6 This sounds great for embedded & real-time applications!

CPU L1 CPU L1 CPU L1 CPU L1 CPU L1 CPU L1 CPU L1 CPU L1 Shared Resources Bandwidth Real-Time Applications on Multicores? L2 Cache Off-chip memory Problems: -- Cache contention -- Bus interference -- Multiprocessor scheduling -- Spinlocks/Queuing -- Cheap/expensive Synchronization 7

CoDeR-MP addressing the challenges:  Migrating legacy software to multicore Sequential code  parallelization Performance issues – memory problems Synchronization/locking problem  Developing new real-time software on multicore High-performance applications: “fast” – real-time applications Predictable real-time applications with guarantees: “correct” and “deterministic” Driven by Industrial Applications

Real-Time Tracking with parallel particle filter – SAAB

Real-Time Control – ABB Robotics ABCD CommandsHigh-level instructions Precise moves Requests Welding program IRC5 robot controller Mixed Hard and Soft Real-Time Tasks 20% hard real-time tasks Main concerns: Isolation between hard & soft tasks: “fire walls” Real-time guarantee for the 20% “super” RT tasks Migration to multicore?

Goals of CoDeR-MP New techniques for High-performance Real-Time applications & Predictable Real Time applications on multi-core processors Mixed applications on the same multi-core chip 20% Hard RT 60% Soft RT 20% Others

Project Plan  Task 1 (Demonstrators) Migration of IRC5 robot controller onto multicore platform ( guidelines and tools for performance and real-time guarantees) Multicore implementation of parallel alg. for ground target tracking  Task 2 (Application diagnostics for migration) Methods and tools for modeling, adaptation, integration and evaluation of design alternatives  Task 3 (Application parallelization) Parallel algorithms for control and signal processing  Task 4 (Resource allocation for real-time/”predictable”) Multicore scheduling (processor cores and caches)  Task 5 (Resource allocation for performance/”fast”) Resource modeling and management

Consortium/Senior Members  SAAB SAAB Systems, Mats Ekman (SAAB Combitech, Björn Holmberg)  ABB Corporate Research, Jan Höglund ABB Robotics, Peter Ericsson/Roger Kulläng  Uppsala University Automated Control, Alexander Medvedev Computer Architectures, Erik Hagersten & David Black-Schaffer Software Technology, Bengt Jonsson Embedded Systems, Wang Yi, Project leader

Current Ph.D. Students  David Eklöv  Guan Nan  Pan Xiaoyue  Andreas Sandberg  Andreas Sembrant  Olov Rosen  Jonatan Liden  Zhang Yi  David Black-Schaffer (now assistant professor) Previous Post Doc Fellow

CoDeR-MP: Project Structure  Techniques/tools for real-time guarantees Wang et al  Techniques/tools for performance guarantees Erik, Bengt et al  Industrial Applications: real-time signal processing Alexander and Mats

Main achievements  Industrial applications SAAB shows great interests in using the parallel signal processing algorithms developed within CoDeR-MP for real-time tracking ABB robotics shows great interests of using the CoDeR-MP performance modeling/profiling tools  Academic research 20 (peer-reviewed) papers on good/top conferences 2 best paper awards: IEEE RTSS 2009 and HiPEAC best paper nominations (IEEE RTSS09, IEEE RTSS10, IEEE RTAS10, IEEE RTAS11 & HiPEAC11) Solved a 37-year open problem for multiprocessor scheduling  Successful FP 7 collaboration, 4 proposals! Wang, CERTAINTY (Mixed embedded applications on multicores), likely to be funded Erik (passed the threshold, cliff-hanger) Wang, Encore (passed the threshold) Bengt (passed the threshold)

Demonstrators (in progress)  Real-Time Tracking Running on “recorded data”  Migration of legacy code Prototype tools for performance analysis Cache coloring on LINUX for real-time guarantee

VISION

Robot Contriller Hard 20% Real-Time Soft Real-Time Non Real-Time: House Keeping We must allocate “resources”: cores, caches We must isolate the different applications

Platform Hardware Resources: Cores, Caches, Memmory Bandwidth … Application Hard real-time, Software real-time, Others ?

Platform Hardware Resources: Cores, Caches, Memmory Bandwidth … Resource Virtualization Hard 20% Real-Time Soft Real-Time Non Real-Time: House Keeping Application Hard real-time, Software real-time, Others Resource Reservation

Platform Hardware Resources: Cores, Caches, Memmory Bandwidth … Hard 20% Real-Time Soft Real-Time Non Real-Time: House Keeping Application Hard real-time, Software real-time, Others Server 1 … … Server N Application Mapping Resource Partition

Platform Hardware Resources: Cores, Caches, Memmory Bandwidth … Hard 20% Real-Time Soft Real-Time Non Real-Time: House Keeping Application Hard real-time, Software real-time, Others Server 1 … … Server N Application Mapping Resource Partition CoDeR-MP tools