Improve timeout handling: skip timed-out algorithms and cap big benchmarks at 15000 #718
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- Replace manual polling loop with Julia's built-in timedwait() function - Avoid using Base.throwto() which can cause hangs with sleeping tasks - Let timed-out tasks continue in background rather than trying to kill them - Use Channels for clean communication between tasks - Close channels properly to prevent resource leaks This fixes the issue where autotune would hang indefinitely when trying to interrupt tasks that exceeded the timeout limit.
…marks - Algorithms that timeout are automatically excluded from larger matrix sizes - This prevents wasting time on algorithms that are already too slow - Cap :big benchmark sizes at 15000 instead of 20000 for stability - 20000x20000 matrices often cause issues even on powerful computers - Add tracking of timed-out algorithms per element type - Report number of skipped algorithms in summary output - Update documentation to reflect these improvements This makes the autotuning process much more efficient by not repeatedly testing algorithms that have already proven to be too slow.
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Summary
This PR improves the efficiency of the autotuning process by:
Changes
1. Smart Algorithm Exclusion
2. Reduced Maximum Matrix Size
vcat(1000:2000:10000, 10000:5000:20000)tovcat(1000:2000:10000, 10000:5000:15000)3. Improved Reporting
Benefits
Example Output
Testing
The improved logic ensures that:
This builds on #716 and #717 to make the autotuning system more robust and efficient.