Convex Optimization in Local Single-Threaded Parallel Mobile Computing Rashid Khogali Olivia Das...
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Transcript of Convex Optimization in Local Single-Threaded Parallel Mobile Computing Rashid Khogali Olivia Das...
![Page 1: Convex Optimization in Local Single-Threaded Parallel Mobile Computing Rashid Khogali Olivia Das Kaamran Raahemifar.](https://reader035.fdocuments.in/reader035/viewer/2022062714/56649cf95503460f949c9fae/html5/thumbnails/1.jpg)
Convex Optimization in Local Single-Threaded Parallel Mobile ComputingRashid KhogaliOlivia DasKaamran Raahemifar
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ScenarioEach processor has
a memory queue that accommodates an arbitrary maximum number of tasks.
Tasks and processors are heterogeneous.
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GoalFind the optimized decision
algorithm. dictates which task goes to which
processing stream. “optimize” means to minimize both time
and energy consumption.
Determine the optimized processing rate of executing each task.
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AssumptionsHeterogeneous processors and
tasksOnlineConstrained processing ratesEnergy cost affected by
remaining energy levelUser determines unit cost of
energy and timeStochastic availabilityMultiple energy sources
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DefinitionsTask Tk = (mk, p μ,k, Bk)
mk :memory requirement in bits.
p μ,k :minimum recommended execution rate of the task.
Bk : number of base instructions.
User Profile Uk = (αε,k, αt,k) αε,k : energy cost sensitivity factor($/Joule)
αt,k : time cost sensitivity factor($/Second)
αε,k is treated with more objectivity than αt,k.
Stream ProcessorPs,j Ps,j : operating frequency (base
instructions/second) p μ,k ≦ Ps,j ≦ PMax,j
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Definitions(cont.)Task’s Energy and Power
Consumptionεk = λj(pk)3tk
tk = Bk / pk εk : expected energy consumption(Joules)
pk : actual execution rate
tk : actual execution time
Bk : task’s number of base instructions
λj : processor energy inefficiency coefficient
εk = λjBk(pk)2
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Constraints
Mm : available memory
(Em,j – Eθ,j) : usable battery energy of jth processing stream
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StepsAssume the potential aggregate
cost of introducing the task to each of the processing streams.
Minimize the aggregate cost function by re-adjusting the processing rates of all tasks in the queue.
Choose the stream with the lowest potential aggregate cost.
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Cost Function Cj : cost of the jth
stream ij : # of task in
queue ε%,j : remaining
power Al,j : availability of
executing Tl in the jth
stream t θ,r,j : overhead
access time of a task Tr to be accessed by Pj
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Cost Function(Cont.)Rearrange the cost functionAssume Ak,j = Aj, ∀k ∈ {1,2, … ,
ij}
otherwise
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Minimizing Cost Function“ i ” dimensional optimization
problem for each stream.Adjustable parameter: pl
Optimize Cj
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Minimizing Cost Function(Cont.)
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Minimizing Cost Function(Cont.)
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Confirm MinimaUse Hessian matrix[1] to confirm
minima.
[1] 海森矩陣: http://zh.wikipedia.org/wiki/%E6%B5%B7%E6%A3%AE%E7%9F%A9%E9%98%B5
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Confirm Minima(Cont.)
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Minimizing Constrained Cost FunctionDon’t forget “p μ,k ≦ Ps,j ≦ PMax,j”
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Single-threading Multi-buffer Scheduling & Processing AlgorithmUser specifies αε,k and αt,k for each
Tk ∈ T.For an arriving task Tk ∈ T,
evaluate and compare the minimum potential processing cost Cmin.
Tk is assigned to stream j* and to be processed at an adjusted optimum processing rate.
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Single-threading Multi-buffer Scheduling & Processing Algorithm(Cont.)Execute T1,j* at rate
Update processing rate whenever a task is either introduced or deleted to Qs,j*.
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Analytical Demonstration
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ConclusionThe authors propose a real-time
multiprocessor scheduling algorithm(SMSP).
The algorithm explicitly finds a globally optimum solution for each aggregate cost function.◦Minimizes the sum of both energy
and execution time of tasks.
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Assume ε%,j does not significant vary or is more or less a constant function of pk.
◦The assumption is valid as long as the condition: εk << E cap,j , is satisfied.