Deadlock Detection for Distributed Process Networks

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Deadlock Detection for Distributed Process
Networks
Alex G. Olson Brian L. Evans The University of
Texas at Austin
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Motivation for Formal Models

• Applications may require higher input/output and
  computational rates than one CPU can handle
• Exploit parallelism for high performance
• Parallel (one machine) or distributed (many
  machines)
• Pitfalls of parallel/distributed programming
• Synchronization, shared memory, and deadlock
• Debugging concurrent code on many processors
• Formal models have provable properties
• Determinacy programs are correct by construction
• Validation only debug each component separately
• Scalability faster execution with more CPUs

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Applications
Application Input Data Rate Computation Rate Output Data Rate
Sonar Beamforming Allen Evans 00 160MB/s 4-20 GFLOPS 72MB/s
Bzip2 (block-zip)Compression 1-4MB/s 1-4 GIPS (approx) 1-4MB/s
MPEG4 Encoding (4CIF) 18MB/s 2 GIPS 1MB/s
H.264 Video Server(QCIF) Banerjee 02 1MB/s 1GIPS 40KB/s
Design Space Exploration Vissers Wolf,
1999 Image Processing Webb et al., 1999
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Process Networks Kahn, 1974

• Concurrently executing processes
• Communicate only over one-wayunbounded channels
  (FIFO queues)
• Read one input port at a time
• Node execution suspended until enough data
  available
• Data that has been read is dequeued from channel
• Samples (tokens) flow along arcs
• Samples have value but not time information
• Flow of (untimed) data drives computation
• Determinate execution
• Any scheduling algorithm that obeys above rules
  will produce same history of tokens on arcs

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Bounding Size of PN Queues

• Bounded Scheduling Parks Lee, 1995
• Write to a full queue suspends node execution
• On global deadlock, resize smallest queue
• Favors incomplete bounded execution
  (non-determinate)
• Computational PN Allen Evans, 2000
• Processes may consume fewer tokens than read
• All memory allocation can be handled by queues
• Bounded Scheduling Geilen Basten, 2003
• Show local deadlock may not lead to global
  deadlock

Artificial deadlock
Deadlock detection required for bounded
communication, but no framework detects local
deadlock
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Deadlock Detection Algorithm

• Mitchell Merritts algorithm 1984
• Detects local and global deadlocks
• Exactly one process detects deadlock
• Simplifies deadlock resolution
• Pair of labels (numbers) used for deadlock
  detection
• Deadlock detected when a label makes a
  round-trip among set of blocked processes

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Mitchell-Merritt Example
BUSY
Write to B
BUSY
Read from C
A
B
B
A
Blocking Step
Initial State
D
C
D
C
BUSY
Read from A
BUSY
Arrows indicate waiting.Artificial deadlock
without feedback.
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Mitchell-Merritt Example
A
B
B
A
D
C
D
C
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Implementation

• Distributed framework for Computational Process
  Networks
• TCP sockets for communication
• Transmit and receive queues (zero-copy)
• C, POSIX threads
• http//www.ece.utexas.edu/bevans/projects/pn

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Execution Performance
Overhead lt1µs per read/write
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Execution Performance
Overhead lt1µs per read/write
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Conclusion

• Formal models simplify parallel design,
  implementation, and debugging
• Communication in PN model follows
  Single-Resource semantics
• Mitchell-Merritt algorithm applicable to
  non-distributed, parallel, and distributed PNs
• Can be used to implement bounded-memory
  scheduling algorithms