Eric Ruppert

Many concurrent algorithms require processes to perform fetch-and-add operations on a single memory location, which can be a hot spot of contention. We present a novel algorithm called Aggregating Funnels that reduces this contention by spreading the fetch-and-add operations across multiple memory locations. It aggregates fetch-and-add operations into batches so that the batch can be performed by a single hardware fetch-and-add instruction on one location and all operations in the batch can efficiently compute their results by performing a fetch-and-add instruction on a different location. We show experimentally that this approach achieves higher throughput than previous combining techniques, such as Combining Funnels, and is substantially more scalable than applying hardware fetch-and-add instructions on a single memory location. We show that replacing the fetch-and-add instructions in the fastest state-of-the-art concurrent queue by our Aggregating Funnels eliminates a bottleneck and greatly improves the queue's overall throughput.

We present a novel linearizable wait-free queue implementation using single-word CAS instructions. Previous lock-free queue implementations from CAS all have amortized step complexity of Ω(p) per operation in worst-case executions, where p is the number of processes that access the queue. Our new wait-free queue takes O(log p) steps per enqueue and O(log² p + log q) steps per dequeue, where q is the size of the queue. A bounded-space version of the implementation has O(log p log(p + q)) amortized step complexity per operation.