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ADaPT: An Event-Passing Protocol for Reducing Delivery Costs in Scatter-Gather Parallel Processes

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ADaPT: An Event-Passing Protocol for Reducing Delivery Costs in Scatter-Gather Parallel Processes

ADaPT: An Event-Passing Protocol

For Reducing Delivery Costs in

Scatter-Gather Parallel Processes

Motivation

Motivation

Established Techniques

ADaPT

Performance Comparison

Conclusions

What is the Laboratory for Neural Dynamics?

- A computational-science section of the Center for Neural Engineering
- Part of a National Science Foundation engineering research center dedicated to biomimetic microelectronic systems
- Combines computational electrophysiology, engineering, pharmacology, and other disciplines
- Integrates empirically-measured, realistic, and biologically-inspired synaptic models for the purposeof temporal signals processing

The Dynamic Synapse

- Biologically-inspired rather than realistic
- Computationally-complex and non-linear
- Signals processing application was originally a proof of concept
- Now a synergistic field for the Center

Postsynapse

Neuron

Presynapse

Na+

Figure 1: Electro-chemical synaptic transmission

Ca2+

Ca2+

Feedback

threshold

threshold

Action Potential input

Glutamate release

Synaptic Potential Summation

Action Potential output

A

AP

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AP

EPSP

EPSP

EPSP

EPSP

EPSP

EPSP

EPSP

EPSP

EPSP

EPSP

EPSP

EPSP

Dynamic Synapse Neural Networks

LAYER 1

- Classical NN structure
- Increased synaptic functionality
- Parameter trainingvia genetic algorithms

Array of K input neurons

LAYER 2

3xK Pre-synaptic Matrix

3xK Post-synaptic Matrix

K

Output Neurons

3

2

1

AP

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Feedback Modulation

Captured Sound

K length filter bank

Microphone Array

Figure 2: 3xK 2-Layer DSNN Single Word Classifier.

ADaPT: An Event-Passing Protocol

For Reducing Delivery Costs in

Scatter-Gather Parallel Processes

Motivation

Motivation

Established Techniques

ADaPT

Performance Comparison

Conclusions

Master

Scatter-Gather

Computation Time

- Naïve Approach
- Multiple SequentialScatter-Gathers
- With uniform computation time, exhibits decent parallelism
- With variable computation times,significant idle time

Worker n -2

Worker n -1

Worker 1

Worker 2

Worker 3

Worker 4

Worker 5

Worker 6

Worker n

Master

Worker n -2

Worker n -1

Worker 1

Worker 2

Worker 3

Worker 4

Worker 5

Worker 6

Worker n

Master

Worker n -2

Worker n -1

Worker 1

Worker 2

Worker 3

Worker 4

Worker 5

Worker 6

Worker n

Master

Figure 3: Multi-phase evaluation of 3n genomes by n workers using naïve scatter-gathering.

A More Efficient Mapping

Master

Computation Time

- Asynchronous scattering
- Reduced idle time for workers
- Closer to optimal time to solution
- Dynamic allocation of resources
- More difficult

Worker n -2

Worker n -1

Worker 1

Worker 2

Worker 3

Worker 4

Worker 5

Worker 6

Worker n

Master

Figure 4: Multi-phase evaluation of 3n genomes by n workers using a more efficient mapping.

ADaPT: An Event-Passing Protocol

For Reducing Delivery Costs in

Scatter-Gather Parallel Processes

Motivation

Motivation

Established Techniques

ADaPT

Performance Comparison

Conclusions

Adaptive Data-parallel Publish/Subscribe Transport Protocol

- Publish/Subscribe
- Worker-centric, i.e. processes subscribe to the master
- Data is transported (published) to workers as events
- Unsubscription is possible

- Two-phase adaptive protocol
- Learning phase: request-reply, monitoring of time between requests
- Aggressive phase: events are pushed to workers at regular intervals

ADaPT: An Event-Passing Protocol

For Reducing Delivery Costs in

Scatter-Gather Parallel Processes

Motivation

Motivation

Established Techniques

ADaPT

Performance Comparison

Conclusions

Message-passing costs for MPI scatters

- Two protocols
- Aggressive & Conservative

- Scatter/Gathers in most implementations use conservative protocols
- Analysis due to Gropp, et. al.

C(MPI Scatter) = (# pop.)[3s + r(n+3e)]

Where n = event payloade = envelope

r = network bandwidth

s = latency

Equation 1: Computation time cost in of scatters

In MPI.

Computational Costs for Multiple scatters in MPI

- Our assumption is a normally distributed population of compute times
- An ideal ordering of computations would be sortedby compute time
- How much idle time is present?

C(Computation) = (# pop.)(avg. compute time) + (# workers)(avg. compute time)

Equation 2: Computation time of a normally-distributed population using scatters in MPI.

Figure 5: Graph of sorted compute times of anormal distribution illustrating idle time.

Message-Passing costs for ADaPT

- Three different costs of event-passing in ADaPT:
- Subscription
- Learning Phase
- Aggressive Phase

C(subscription) = (# workers) x [s + re]

C(learning) = (# samples) x [2s + r(n+2e)]

C(aggressive) = (# pop - # samples) x

[s + r(n+e)]

Note: we assume control events to be

of size e

Equation 3: Event-passing costs of ADaPT.

Unsubscribe Costs for ADaPT

- An unsubscribe occurs when a worker’s event buffer is in danger of overflowing
- With ADaPT, an overflowoccurs when a worker receivesm-1 events triggering computetimes greater than the estimatedaverage (assuming a worker buffers m events)
- Conservatively, we have decidedthat workers should clear theirbuffers before resubscribing

- We used a Monte Carlo simulation (details in paper) to determine E,the % pop with compute times > than the estimated mean given error as a function of % pop. sampled

P(unsubscribe) =

E*Pop C m-1

Pop C m-1

C(unsubscribe) = P(unsubscribe) x

[2(s+re) + (m-1)(avg. compute+ Δ)]

Equation 4: Costs of worker unsubscription in ADaPT.

Analysis

(# pop - # samples)2re + (# samples)(avg. compute time) >(# pop / m) x P(unsubscribe) x

[2re + (m-1)(avg. compute time)]

- Which protocol is more appropriate?
- For simplicity of comparisonwe will drop the latency termand assume the number of samples to be equal to thenumber of workers
- i.e. each worker’s firstcomputation is monitored

Equation 5: Cost comparison of MPI vs. ADaPT.

Figure 6: Graph of inequality in Equation 5.

ADaPT: An Event-Passing Protocol

For Reducing Delivery Costs in

Scatter-Gather Parallel Processes

Motivation

Motivation

Established Techniques

ADaPT

Performance Comparison

Conclusions

What have we shown?

- ADaPT is useful when multiple scattering of data must occur due to natural aggregation
- An example is the training of the DSNN using genetic algorithms

- Worker-centric approach for reduced processor idle time
- Unsubscription is expensive but can be avoided withgreater event-buffering capabilities
- ADaPT exploits an event pattern which emerges fromthe application of a well-known architectural pattern

Thank You