Inbyggda realtidssystem: En flerkärning frälsning Embedded real-time systems: A multi-core salvation Thomas Nolte,

1997 – 2000 – 2006 – 2009 – 2012 Undergrad. Student Grad./PHD Student Assistant Professor Associate Professor ABB CRC , 2006

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5 Embedded systems

Environment Control program sensors actuators Task CPU Key properties of interest = performance & predictability 6 Embedded systems

LINK

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Too EARLY inflationToo LATE inflationPerfect TIMING

Environment Control program sensors actuators Task When software is executing, it is executing as tasks void foo() { … while(status) { in = read_sensor(); action = take_action(in); … status = perform_action(action); sleep(1000); } Task 11

12 Instruction and data memory (1)

13 Instruction and data memory (2) DATA INSTRUCTION void foo() { … while(status) { in = read_sensor(); action = take_action(in); … status = perform_action(action); sleep(1000); } Task

14 Instruction and data memory (3) cache memory

15 Instruction and data memory (4) cache memory Key properties of interest = performance & predictability

A simple model Environment Control program sensors actuators Task task time period worst-case execution time 16

What to run and when Environment Control program sensors actuators Task task 1 time task 2 time task 2 prio low prio high time task 2 task 1 The scheduler decides which task to execute 17

Determining the response time Environment Control program sensors actuators Task time task 2 task 1 preempted resumed finished response time of task 1 response time of task 2 18

Predictability wrt embedded control systems ●Program, or task ●Instruction for how the computer should work, i.e., what the computer shall do ●Execution time ●How long time it takes for the program/task to execute ●Response time ●How long time it takes for the program/task to execute to completion, also when running together with other programs/tasks ●Research conducted the past 30 years Pretty good understanding how predictable embedded systems should be constructed 19

The multi-core arrived! ●Isn’t more better?

The Free Lunch Is Over A Fundamental Turn Toward Concurrency in Software By Herb Sutter 21

22 The multi-core revolution single coredual corequad coremulti core parallel computer = many cores

23 Multi-core memory hierarchy (1)

24 Multi-core memory hierarchy (2)

Challenges with many cores ●Sequential guarantee is not valid anymore ●Programs could previously execute in parallel, but at any given time only one program is executing ●Now at any given point in time as many programs as there are cores can be executing 25 pgm 1 time pgm 2 time pgm 2 Core 1 Core 2 pgm 2 pgm 1 time Core

Performance on a parallel machine (1) ●The way we construct programs become different chefs do not bake 1 cake 100x faster than 1 chef

Performance on a parallel machine (2) ●The way we execute programs become different 27 pgm 1 time Core pgm pgm 2 51

Performance on a parallel machine (3) ●The way we execute programs become different 28 pgm 1 time pgm 2 time Core 1 Core 2 pgm %

Performance on a parallel machine (4) ●The way we execute programs become different 29 time pgm 2 time Core 1 Core 2 pgm 1 pgm j

Predictability under parallelism (1) ●New techniques are required to resolve conflicts 30 pgm 1 time pgm 2 time pgm 2 Core 1 Core 2

Predictability under parallelism (2) ●New techniques are required to resolve conflicts 31 pgm 1 time pgm 2 time pgm 2 Core 1 Core 2 shared memory

Predictability under parallelism (3) ●New techniques are required to resolve conflicts 32 pgm 1 time pgm 2 time pgm 2 Core 1 Core 2 shared memory

…and the many-core is here! ●More is better?

34 Epophany TM Multi-core Solution

single core dual core many core empty core77 task scheduling task partitioning task scheduling local global task partitioning task scheduling idle? assume 7 tasks: 35 New challenges

task 0 task 1 task 2 task 3 task 4 task 5 task 6 task 7 task 8 task 9 Less shared state and data among cores Less shared state and data among cores Locality important when sharing data Locality important when sharing data More message passing (via powerful interconnects) More message passing (via powerful interconnects) Sleep cores when idle to save powerSleep cores when idle to save power Less shared state and data among cores Less shared state and data among cores Locality important when sharing data Locality important when sharing data More message passing (via powerful interconnects) More message passing (via powerful interconnects) Sleep cores when idle to save powerSleep cores when idle to save power

Our efforts wrt. multi/many-core ●New ways to program and analyze ●Meng Liu (PhD Student) ●Hamid Reza Faragardi (PhD Student) ●New ways to partition and design ●Moris Behnam (Senior Lecturer) ●Daniel Hallmans (PhD Student) ●New ways to execute ●Mikael Åsberg (PhD Student) ●Nima M. Khalilzad (PhD Student) ●New ways to communicate and synchronize ●Mohammad Ashjaei (PhD Student) ●Sara Afshar (PhD Student) 37

PhD student Post-doc Key CORE main research areas (2013):  Multi-core and many-core real-time systems  Real-time systems scheduling and synchronization  Predictable execution of real-time systems  Compositional execution and analysis of real-time systems  Simulation-based analysis of embedded systems  Stochastic and statistical analysis of real-time systems  Real-time communications  Adaptive and reconfigurable real-time systems CORE alumni: The Complex Real-Time Embedded (CORE) research group: Mikael Åsberg Nima M. KhalilzadDaniel HallmansMohammad AshjaeiSara AfsharMeng Liu Kristian Sandström Moris BehnamThomas Nolte Hamid R. Faragardi TBD Insik Shin Holger KienleJohan KraftFarhang NematiYue LuAnders Wall

Embedded real-time systems: A multi-core salvation Thomas Nolte,