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This paper presents our solution to the DEBS 2015 Grand Challenge. The analysis of the Grand Challenge is partitioned among an arbitrary number of processing units by leveraging ScaleGate, a recently proposed abstract data type with its concurrent implementation which articulates data access in parallel data streaming. ScaleGate aims not only at supporting high throughput and low latency parallel streaming analysis, but also at guaranteeing deterministic processing, which is one of the biggest challenges in parallelizing computation while maintaining consistency.

Helping is the most common mechanism to guarantee lock-freedom in many concurrent data structures. An optimized helping strategy improves the overall performance of a lock-free algorithm. In this paper, we propose help-optimality, which essentially implies that no operation step is accounted for exclusive helping in the lock-free synchronization of concurrent operations. To describe the concept, we revisit the designs of a lock-free linked-list and a lock-free binary search tree and present improved algorithms. Our algorithms employ atomic singleword compare-and-swap (CAS) primitives and are linearizable. Additionally, we do not use a language/platform specic mechanism to modulate helping, specically, we use neither bit-stealing from a pointer nor runtime type introspection of objects, making the algorithms language-portable. Further, to optimize the amortized number of steps per operation, if a CAS execution to modify a shared pointer fails, we obtain a fresh set of thread-local variables without restarting an operation from scratch. We use several micro-benchmarks in both C/C++ and Java to validate the eciency of our algorithms against existing state-of-the-art. The experiments show that the algorithms are scalable. Our implementations perform on a par with highly optimized ones and in many cases yield 10%-50% higher throughput.

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