Dynamically detecting and tolerating IF-Condition Data Races

An IF-Condition Invariance Violation (ICIV) occurs when, after a thread has computed the control expression of an IF statement and while it is executing the THEN or ELSE clauses, another thread updates variables in the IF’s control expression. An ICIV can be easily detected, and is likely to be a sign of a concurrency bug in the code. Typically, the ICIV is caused by a data race, which we call IF-Condition Data Race (ICR). In this paper, we analyze the data races reported in the bug databases of popular software systems and show that ICRs occur relatively often. Then, we present two techniques to handle ICRs dynamically. They rely on simple code transformations and, in one case, additional hardware help. One of them (SW-IF) detects the races, while the other (HW-IF) detects and prevents them. We evaluate SW-IF and HW-IF using a variety of applica-tions. We show that these new techniques are effective at finding new data race bugs and run with low overhead. Specifically, HW-IF finds 5 new (unreported) race bugs and SW-IF finds 3 of them. In addition, 8-threaded executions of SPLASH-2 codes show that, on average, SW-IF adds 2 % execution overhead, while HW-IF adds less than 1%. 1

BulkCompactor: Optimized Deterministic Execution via Conflict-Aware Commit of Atomic Blocks ∗

Recent proposals for determinism-enforcement architectures are able to honor the dependences between threads through a commit step that often becomes a performance bottleneck. As they commit code blocks (or chunks) in a round-robin order, if one chunk gets squashed due to a conflict, its successors also observe a stall. We call this effect transitive squash delay. This paper proposes a novel, high-performance approach to deterministic execution based on Conflict-Aware commit. Rather than committing chunks in strict round-robin order, the idea is to skip those chunks with conflicts and deterministically execute them slightly later. The scheme, called BulkCompactor, largely eliminates transitive squash delay, “compacts ” the chunk commits, and substantially speeds-up execution. With BulkCompactor, the squash overhead is O(N) rather than O(N 2) as in round-robin. We describe BulkCompactor designs for machines with centralized or distributed commit. Finally, a simulation-based evaluation shows that BulkCompactor delivers performance comparable to nondeterministic systems. For example, for 32 processors, BulkCompactor incurs an average execution overhead of 22 % over a nondeterministic system. The round-robin scheme’s average overhead is 133%. 1