Programming many core systems
Marco Bekooij
Outline
Definition many core systems
Application domain of many core systems
Microsoft Parallel Computing Initiative
– simplify programming
– improve quality of service
Mapping stream processing on real-time multiprocessor systems
– Automatic parallelization
– Budget computation
– Multiprocessor system hardware design with budget enforcement
Conclusion
Definition many core system according to Intel’s
white paper
Many core systems are multiprocessor systems with a large number of
cores (>8)
– Many core systems have a shared address space and its resources are
under control of the operating system
Computing industry shifts and their effects
on user experience
Parallel computing and the next generation of
user experiences
Microsoft: many core applications
Next-Generation Personal Computing Experiences
– Personal modeling: e.g. “walk-through” 3-dimensional, photo-realistic
renderings of a home renovation
– Personalized adaptive learning: create a personalized, context-aware
curriculum in real-time.
– Public safety: detailed 2- or 3-dimensional renderings, object recognition,
help responders to make well-informed decisions critical to rescue tactics,
evacuations, and emergency response
Business Opportunities
– Financial modeling
– Product design simulation
Many core application example of NXP
Multi-stream multi-standard car-infotainment systems
– Advanced radios contain already 13 processors (10 DSPs + 3 µP) +
number of hardware accelerators
Beamforming
Improved radio reception
Microsoft Parallel Computing Initiative
Objectives:
• simplify parallel software development
• take quality-of-service requirements into account
Microsoft Parallel Computing Initiative
Applications: next experiences, improve productivity
Domain libraries: system building blocks for example imageprocessing libraries
Programming models and languages: easy application development
without the need for expert knowledge
Developer tooling: simplify software integration
Runtime, platform, and operating systems: more effectively budget
and arbitrate among competing requests for available resources in the
face of parallelism and quality-of-service demands. Additionally,
Microsoft will continue to improve the reliability and security of the
platform.
NXP’s application domain: real-time stream
processing
use-case
f2
f1
ADC
PDC
CFE
VIT
CBE
SRC
APP
digital radio job A
f3
BR
use-case
MP3
SRC
source decoding job B
APP
DAC
DAC
Software mapping flow for real-time stream
processing applications
Temporal constraints
Architecture instance
NLP
Omphale
(parallelization)
Task graph +Dataflow graph
Execution time
analysis
Task-graph + Dataflow graph
Hebe
(budget computation)
Task-graph + budgets
Use-cases + transitions
Off-line = at design-time
On-line = at run-time
Helios
(resource allocation)
Table with resource allocations
Start/stop job
Minos
(resource assignment)
Preemptive
kernel
FIFO
com lib
Run-time mapping of tasks to processors
with admission control per job
Temporal constraints
Architecture instance
NLP
Omphale
(parallelization)
Task graph +Dataflow graph
Execution time
analysis
Task-graph + Dataflow graph
Hebe
(budget computation)
Task-graph + budgets
Off-line = at design-time
On-line = at run-time
Start/stop job
Minos
(resource assignment)
Preemptive
kernel
FIFO
com lib
Setting computation requires property
preserving abstraction
Budget
computation
Dataflow model
v1
v0
v2
 Abstraction
DSP
P
$
External
SDRAM
I/O
ctrl
mem
NI
NI
Network
NI
NI
Experimental predictable many core system
Timer
Blaze
ROM
MEM
Timer
Blaze
ROM
$
MEM
RS232
$
Aethereal NoC
SDRAM ctrl
Distributed shared memory system
– Pthread support
SDRAM
Budget scheduler for every shared resource
– processors, memory ports, inter-connect
Flow control
– back-pressure
Mapped on a Vitex 4 FPGA
Heterogeneous many core system
Timer
DSP
ROM
IMEM
Timer
Blaze
ROM
DMEM
MEM
RS232
$
Aethereal NoC
Heterogeneous for area-efficiency and
power-efficiency reasons
Streaming without addresses over the
network beside address based streaming
SDRAM ctrl
SDRAM
Essential elements in the approach
Key assumption: characteristics of other jobs are not completely known
at design time:
– Other jobs are downloaded
– Worst-case execution times of the tasks are not known at design time
Essential element
– Budget schedulers
– Flow control
Budget schedulers
All schedulers
Budget
schedulers
Budget scheduler: subclass of the aperiodic server
– minimum budget in a replenishment interval is independent of the execution-time and
event arrival-rate
Budget reservation:
– incomplete knowledge: worst-case execution times of the tasks of other jobs are not
known
– overload protection: estimated execution times are optimistic
Budget scheduler example:
time division multiplex
x(j): execution time of the j-th execution, P: period, B: slice length
Budget scheduler with priorities: PBS
• number of preemptions in a RI is fixed  preemption overhead is known
• maximum time between event and start of high priority task with budget
Flow control
Data can be lost without flow control  non deterministic functional behavior
Buffer overflow
P
Buffer overflow can occur if:
C
– best-case execution time of producer P is over estimated
– worst-case execution time of consumer C is under estimated
Task graph and dataflow graph extration
Extraction of a task graph is difficult
– Data dependency analysis
Derivation of an (dataflow) analysis model of an application is difficult an
error prone
– No one-to-one correspondence between task graph and dataflow graph
Our approach: describe top-level of the application as a nested loop
program
– Allow while loops, if conditions, and non-affine index expressions
Nested loop programs (NLPs)
A nested loop program is specified in a coordination language
– Specifies dependencies (communication) between functions
– functions are defined in a programming language, e.g. C
– to simplify/enable parallelization
• many programming language constructs are not supported to simplify/enable
analysis
• new program language constructs have been added to improve the
analyzability (and therefore NLPs are not an C-subset)
Nested loop programs should be seen as a sequential specification of a
task graph
– single assignment
• each array location is written at most once during one execution of the outer
while loop
– functions must be side-effect free
Nested loop program example
mode=0;
while(1){
in=input();
switch(mode){
case 0: {mode=detect(in); }
case 1: {mode,o1=decode1(in); o2=decode2(o1); output(o2);}
}
}
Resulting task graph
det
in
dec1
dec2
out
Every function becomes a task
Buffers can have multiple readers
Buffers can have multiple mutual exclusive writers
– That writes are mutual exclusive is explicit in the NLP but not in the task-graph
Budget computation
Budgets are computed given real-time constraints
– only end-2-end constraints are imposed by the environment
• throughput + latency and not the deadlines of the tasks
Requires an suitable analysis model for real-time applications
– should take pipelining into account
• the i-th input sample is consumed before the (i-1)th output sample is produced
We apply dataflow analysis with measured (not worst-case) execution
times
Definition real-time system
Real-time systems are those systems in which the correctness of the
system depends not only on the logical results of computation, but also
on the time at which the results are produced.
Real-time analysis
Use of measured execution times instead of worst-case execution times
– Guarantees?
– Load hypothesis
Basics of dataflow analysis
Distinguishing features of real-time systems
High level of determinism:
– It should be possible to derive useful properties of the system, given the
stated assumptions and the information available with an acceptable effort
and a useful accuracy
Concurrency:
– Deal with the inherent physical concurrency
– Deal with a concurrent description of the system
– Deal with a concurrent implementation of the system
Emphasis and significance of reliability and fault tolerance:
– Reliability is the probability that a system will perform correctly over a given
period of time
– Fault tolerance is concerned with the recognition and handling of failures
Computer Assisted Control
Definition predictability
Is should be possible to show, demonstrate, or prove that requirements
are met subject to assumptions made, for example, concerning failures
and workloads.
Note that:
– predictability is always subject to the underlying assumptions made
Real-time system classification
Note that:
• no deadlines are defined for best-effort tasks
• assumes that all tasks in the system have the same criticality
Criticality spectrum for systems
Hard RT
Very critical
Firm RT
Soft RT
Best effort
Not critical at all
Load hypothesis
Statement about the assumption of the peak load of the system
Translates often in an assumption about the worst-case execution times
of the tasks
Difference between guarantee and a
statistical assertion
A guarantee is an assurance of a fulfillment of a condition
– a guarantee is binary statement
– guarantees about the reality are given under certain assumptions
Statistical assertion is a statement about a probability of an occurrence
Focus is on analysis techniques that result in guarantees
– guarantees are given under explicit and testable assumptions (in our case
the load hypothesis)
Research Schools
1.
Real-time system theory should help to give guarantees about the temporal
behavior of the system
Testing can provide only a partial verification of the behavior. This justifies the use of
analytical techniques that can provide complete coverage.
Classical view of the real-time community
2.
Real-time system theory should provide means to manage the system
resources such that the temporal behavior improves
3.
Real-time system theory should provide means to compute system settings
4.
Real-time system theory should provide means to reduce the verification effort
5.
Real-time system theory should provide means to improve the robustness of a
system
Load hypothesis for firm real-time systems
Often execution times are measured instead of computed with WCET
tools
– reason: WCET tools are not available or computed WCETs are overly
pessimistic
Typically a load hypothesis is defined which states that the execution
times of the tasks are not larger than the WCETs used during analysis
Given that the load hypothesis holds we can guarantee with analysis
techniques that no deadlines are missed
If the load hypothesis does not hold then no statements can be made
about the worst-case temporal behavior of the system
Assumption coverage
Strength of materials theory
– Model is for example an approximation of a bridge
• E.g. the stiffness of the metal beam is intrinsically not exactly known, i.e. can be
worse or better
– However model can be a useful approximation of the reality
• Added safety margin (head room) is based on experience
Does the same reasoning apply to real-time system design?
False  useful results?
Usefulness real-time analysis results even
given unsafe execution time estimates
Deadlock freedom and functional determinism of the application
Estimates of the real-time behavior of the tasks
Estimates of appropriate system configuration and system settings
Trends and anomalies
Responsiveness improvement
Sensitivity reduction
Robustness improvement
Synchronization and scheduling overhead reduction
Focus of real-time analysis techniques
Single processor
– Focus is on task scheduling of independent tasks + OS-kernel design
Multiprocessor
– Focus is on throughput and latency analysis of applications described as
task graphs
• also synthesis of settings & budget such that throughput and latency constraints
are met
Formal models for real-time analysis
Process algebra
– Algebra for communicating processes
– Allows transformation of one system into another
Temporal logics
– Propositional logic augmented by tense operators
– System representation with global states become prohibitive large
Automata
– Mathematical model for a finite statemachines
– Synchrony timing hypothesis OR clocks
• instantaneous broad-cast
• system evolves faster than events
Petri nets
– Dataflow graphs have similarities with Petri nets
Classical timing verification techniques
Logic based approach
– Deductive proof: IS or decision procedure Inot(S) is unsatisfiable
– Very high computational complexity and hard to automate
Automata based approach
– Language containment: Li  Ls
– State explosion
Model checking
– State explosion
– High computational complexity
Outside scope of these formal verification
techniques
Techniques to include resource sharing
– effects of scheduling on the temporal behavior
Techniques to make an abstraction of the system while preserving properties
Techniques to synthesize properties instead of checking properties
Techniques to trade accuracy for lower computational complexity
Techniques to trade expressivity model for analyzability
Techniques to trade generality model for analyzability
However these techniques are essential for real-time multiprocessor system
design
– borrow ideas from performance analysis of communication networks
– Latency rate-analysis  dataflow analysis
Rate based analysis
Rate based analysis determines, loosely speaking, the throughput of the
system
Three approaches:
1. Graph-based techniques: maximum cycle mean analysis
2. Algebraic techniques: determine eigenvectors with max-plus algebra
– Stochastic approaches: Markov process
Limitations:
–
–
–
1 and 2 assumes fixed timing delays instead of intervals, while 3 computes the for
real-time systems not very useful long term average throughput
supported models do not support any choice
supported models do not support inputs and outputs
Still useful for real-time analysis purposes? (yes)
Are there solutions available to analyze data dependent applications? (yes)
Related work: worst-case performance
analysis of communication networks
Objective compute maximum latency and minimum throughput for a flow of packets
Links between routers are shared by flows
No flow control: input buffers must be large enough such that overflow does not occur
Related work: worst-case performance
analysis
[R. Cruz, 1991]: A Calculus for Network Delay
– No flow control, does not require starvation free schedulers
– Bound traffic for t0 with a non-decreasing function
[K. Tindell and J. Clark, 1994]: Holistic Schedulability Analysis
– No flow control, static priority preemptive
– fixed point iteration in case of cyclic resource dependencies
[D. Stiliadis et.al., 1998]: Latency-Rate Servers: A General Model for Analysis of
Traffic Scheduling Algorithms
– No flow control
– Requires starvation-free schedulers  there are no cyclic resource dependencies
– System can be characterize without knowledge about the input traffic
•
use of the concept of busy periods
– More accurate estimate of the end-to-end delay than [Cruz91] and [TC94]
[J.Y. Le Boudec, 1998]: Application of Network Calculus to Guaranteed Service
Networks
– No flow control
– Does not require schedulers to be starvation-free
– More accurate estimate of the end-to-end delay than [Cruz91] and [TC94]
Related work: worst-case performance
analysis of task graphs
[RZJE02, JRE02] Event model composition
– Definition of period+jitter traffic models, tasks with AND-condition, generalization of
[TC94]
[S. Chakraborty et.al., 2003] A General framework for ....
– Generalization network calculus, also known as real-time calculus
– Bound traffic and service for any interval t
– Acyclic task graphs
[M.Wiggers, et.al., 2007] Modeling Run-Time Arbitration by Latency-Rate
Servers in Data Flow Graphs
– Requires starvation-free schedulers
– Applicable in case of arbitrary deterministic task graphs: AND, cyclic task graphs,
buffer capacity can be given or computed
[M.Wiggers, et.al., 2009] Monotonicity and run-time scheduling
– Generalization of [M.Wiggers, and M. Bekooij, 2007]: allows sequence of execution
times and any deterministic dataflow graph
– not based on busy periods
[L. Thiele, and M. Stoimenov, 2009] Modular performance analysis of cyclic
dataflow graphs
– Generalization real-time calculus [S. Chakraborty et.al., 2003] : Analysis of cyclic
HSDF graphs
Related work: worst-case performance
analysis of task graphs
[J. Staschulat, et.al. 2009] Dataflow models for shared memory access
latency analysis
– piece-wise linear service approximation of priority based budget schedulers
[M. Wiggers, et.al., 2010] Simultaneous Budget and Buffer Size
Computation for Throughput-Constrained Task Graphs
– only HSDF graphs
– algorithm has a polynomial computational complexity
Dataflow analysis primer
Elements in the single-rate dataflow model
Actors
An actor is depicted as a node
An actor is stateless
An actor can represent a function
An actor can be use to represent a task (but also other things)
Actor fire (a task execute)
Actors have a firing conditions
A firing duration can be associated with an actor
Actors only interact with their environment through token consumption
from their input queues and token production through their output
queues
Queues
A queue is represented by an edge
Queues have per definition an unbounded capacity
Tokens are stored in a queue
Tokens can be consumed from a queue in the order that they are
produced
Tokens
A token is an undividable element
Tokens can be use to represent: data, space, or synchronization
moments
Firing rule
A firing rule is a condition that prescribes the number of tokens that
must be present in the input queues of an actor before the actor can
fire.
The firing rule of a single rate dataflow actor (also called HSDF actor) is:
– one token in each input queue
Notice: a firing rule cannot specify anything about the number of tokens
in an output queue
Throughput calculation example
What is the throughput during self-timed execution?
Token arrival times during self-timed
executed HSDF
Given an HSDF graph G(V,E)
The self-timed execution of this graph has some important properties if:
– The graph is strongly connected, i.e. there is a directed path from every
node to every other node in the graph
– Actors have a constant firing duration
It can be shown that the graph enters a periodic regime after an initial
transition phase
[BCOQ92]
Multi-dimensional periodic schedule
On average every  a firing
Maximum cycle mean
[Rei68]
Maximum cycle mean example
MCM number example (1)
The critical cycle determines the mcm
The nodes and edges colored red belong to the critical cycle
MCM number example (2)
Overlapping firings
MCM calculation example
What is the mcm of this graph?
Related work: worst-case performance
analysis of communication networks
Objective compute maximum latency and minimum throughput for a flow of packets
Links between routers are shared by flows
No flow control: input buffers must be large enough such that overflow does not occur
Flow control
Related work: worst-case performance
analysis
[R. Cruz, 1991]: A Calculus for Network Delay
– No flow control, does not require starvation free schedulers
– Bound traffic for t0 with a non-decreasing function
[K. Tindell and J. Clark, 1994]: Holistic Schedulability Analysis
– No flow control, static priority preemptive
– fixed point iteration in case of cyclic resource dependencies
[D. Stiliadis et.al., 1998]: Latency-Rate Servers: A General Model for Analysis of
Traffic Scheduling Algorithms
– No flow control
– Requires starvation-free schedulers  there are no cyclic resource dependencies
– System can be characterize without knowledge about the input traffic
•
use of the concept of busy periods
– More accurate estimate of the end-to-end delay than [Cruz91] and [TC94]
[J.Y. Le Boudec, 1998]: Application of Network Calculus to Guaranteed Service
Networks
– No flow control
– Does not require schedulers to be starvation-free
– More accurate estimate of the end-to-end delay than [Cruz91] and [TC94]
Related work: worst-case performance
analysis of task graphs
[RZJE02, JRE02] Event model composition
– Definition of period+jitter traffic models, tasks with AND-condition, generalization of
[TC94]
[S. Chakraborty et.al., 2003] A General framework for ....
– Generalization network calculus, also known as real-time calculus
– Bound traffic and service for any interval t
– Acyclic task graphs
[M.Wiggers, et.al., 2007] Modeling Run-Time Arbitration by Latency-Rate
Servers in Data Flow Graphs
– Requires starvation-free schedulers
– Applicable in case of arbitrary deterministic task graphs: AND, cyclic task graphs,
buffer capacity can be given or computed
[M.Wiggers, et.al., 2009] Monotonicity and run-time scheduling
– Generalization of [M.Wiggers, and M. Bekooij, 2007]: allows sequence of execution
times and any deterministic dataflow graph
– not based on busy periods
[L. Thiele, and M. Stoimenov, 2009] Modular performance analysis of cyclic
dataflow graphs
– Generalization real-time calculus [S. Chakraborty et.al., 2003] : Analysis of cyclic
HSDF graphs
Related work: worst-case performance
analysis of task graphs
[J. Staschulat, et.al. 2009] Dataflow models for shared memory access
latency analysis
– piece-wise linear service approximation of priority based budget schedulers
[M. Wiggers, et.al., 2010] Simultaneous Budget and Buffer Size
Computation for Throughput-Constrained Task Graphs
– only HSDF graphs
– algorithm has a polynomial computational complexity
Classical dataflow models
Fundamental differences dataflow models
HSDF: Homogenous synchronous dataflow model
– single-rate, polynomial MCM-algorithm
SDF: Synchronous dataflow model
– multi-rate, graph consistency, no known polynomial MCM-algorithm
CSDF: cyclo-static dataflow model
– fixed number of phases
FDDF: functional deterministic dataflow model
– data-dependent sequential firing rules, Turing complete (halting/deadlock)
DDF: dynamic dataflow model
– non-deteriministic firing rules
Dataflow model subclasses
DFM=data flow model subclass
If DFMA  DFMB then DFGA DFMA  DFGA DFMB
Recently introduced dataflow models with
data dependent quanta
Variable rate dataflow (VRDF) and variable rate phased dataflow
(VPDF) is Turing complete  how can we deal with undecidability?
Hasse diagram representation including
some recently introduced dataflow models
Da  Db if for every dataflow graph da  Da it holds that da  Db
Conclusion
Many core systems are
– shared address space multiprocessor systems
– number of core >8
A large number of cores put more stress on:
– system programming effort
– quality of service aspect
Ongoing research effort on:
– parallelization of a sequential description of stream processing algorithms
– resource management:
• computation of budgets
• enforcement of budgets
Computation of budgets
– focus is on dataflow analysis techniques, challenges include
• data dependent behavior (+ adaptation of resource budgets)
• tradeoff between run-time and computational complexity