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Iterative Context Bounding for Systematic Testing of Multithreaded Programs Madan Musuvathi Shaz Qadeer Microsoft Research

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Testing multithreaded programs is HARD Specific thread interleavings expose subtle errors Testing often misses these errors Even when found, errors are hard to debug No repeatable trace Source of the bug is far away from where it manifests

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Current practice Concurrency testing == Stress testing Example: testing a concurrent queue Create 100 threads performing queue operations Run for days/weeks Pepper the code with sleep( random() ) Stress increases the likelihood of rare interleavings Makes any error found hard to debug

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CHESS: Unit testing for concurrency Example: testing a concurrent queue Create 1 reader thread and 1 writer thread Exhaustively try all thread interleavings Run the test repeatedly on a specialized scheduler Explore a different thread interleaving each time Use model checking techniques to avoid redundancy Check for assertions and deadlocks in every run The error-trace is repeatable

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State space explosion x = 1; y = 1; x = 1; y = 1; x = 2; y = 2; x = 2; y = 2; 2,1 1,0 0,0 1,1 2,2 2,1 2,0 2,1 2,2 1,2 2,0 2,2 1,1 1,2 1,0 1,2 1,1 y = 1; x = 1; y = 2; x = 2; Init state: x = 0, y = 0

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x = 2; … y = 2; x = 2; … y = 2; State space explosion x = 1; … y = 1; x = 1; … y = 1; … n threads k steps each Number of executions = O( n nk ) Exponential in both n and k Typically: n 100 Limits scalability to large programs (large k)

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Techniques Iterative context bounding Strategy for searching large state spaces State space optimization Reduces the size of the state space

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x = 1; if (p != 0) { x = p->f; } x = 1; if (p != 0) { x = p->f; } Iterative context bounding Prioritize executions with small number of preemptions Two kinds of context switches: Preemptions – forced by the scheduler e.g. Time-slice expiration Non-preemptions – a thread voluntarily yields e.g. Blocking on an unavailable lock, thread end x = p->f; } x = p->f; } x = 1; if (p != 0) { x = 1; if (p != 0) { p = 0; preemption non-preemption

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Iterative context-bounding algorithm The scheduler has a budget of c preemptions Nondeterministically choose the preemption points Resort to non-preemptive scheduling after c preemptions Run each thread to the next yield point Once all executions explored with c preemptions Try with c+1 preemptions Iterative context-bounding has desirable properties Property 0: Easy to implement

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Property 1: Polynomial state space n threads, k steps each, c preemptions Number of executions <= nk C c. (n+c)! = O( (n 2 k) c. n! ) Exponential in n and c, but not in k x = 1; … y = 1; x = 1; … y = 1; x = 2; … y = 2; x = 2; … y = 2; x = 1; … x = 1; … x = 2; … x = 2; … y = 1; … y = 1; … y = 2; Choose c preemption points Permute n+c atomic blocks

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Property 2: Deep exploration possible with small bounds A context-bounded execution has unbounded depth A thread may execute unbounded number of steps within each context Can reach a terminating state from an arbitrary state with zero preemptions Perform non-preemptive scheduling Leave the number of non-preemptions unbounded

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Property 3: Coverage metric If search terminates with c preemptions, any remaining error must require at least c+1 preemptions Intuitive estimate for the complexity of the bugs remaining in the program the chance of their occurrence in practice

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Property 4: Finds the ‘simplest’ error trace Finds the smallest number of preemptions to the error Number of preemptions better metric of error complexity than execution length

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Property 5: Lots of bugs with small number of preemptions ProgramKLOCMax Num Threads Bugs Reachable with Preemption Count 0123Total Bluetooth0.4301001 Work-Stealing Queue 1.3301203 Transaction Manager 7.0200213 APE18.94211-4 Dryad Channels16.05151-7

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Most states are covered with small number of preemptions

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Coverage vs Time (Dryad)

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Techniques Iterative context-bounding Strategy for searching large state spaces State space optimization

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Optimization for race-free programs Insert context-switches only at synchronization points Massive state-space reduction Num steps (k) = num synch. operations (not memory accesses) Run data-race detection to check race-free assumption Goldilocks algorithm [PLDI ’07] implemented for x86 Theorem: When search terminates for context-bound c Either find an erroneous execution Or find a data-race Or the program has no errors reachable with c preemptions

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Conclusion Iterative context-bounding algorithm Effective search strategy for multi-threaded bugs Exposes many concurrency bugs Implemented in the CHESS model checking tool Applying CHESS to Windows drivers, SQL, Cosmos, Singularity Visit http://research.microsoft.com/projects/CHESS/

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Extra Slides

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Partial-order reduction Many thread interleavings are equivalent Accesses to separate memory locations by different threads can be reordered Avoid exploring equivalent thread interleavings

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Optimistic dynamic partial-order reduction Algorithm [Bruening ‘99] : Assume the program is data-race free Context switch only at synchronization points Check for data-races in each execution Theorem [Stoller ‘00] : If the algorithm terminates without reporting races Then the program has no assertion failures Massive reduction: k = number of synchronization accesses (not memory accesses)

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Combining with context-bounding Algorithm: Assume the program is data-race free Context switch only at synchronization points Explore executions with c preemptions Check for data-races in each execution Theorem: If the algorithm terminates without reporting races, Then the program has no assertion failures reachable with c preemptions Requires that a thread can block only at synchronization points

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Thomas Ball Principal Researcher Microsoft Corporation Sebastian Burckhardt Researcher Microsoft Corporation Madan Musuvathi Researcher Microsoft.

Thomas Ball Principal Researcher Microsoft Corporation Sebastian Burckhardt Researcher Microsoft Corporation Madan Musuvathi Researcher Microsoft.

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