Deepthi Devaki Akkoorath
PhD thesis
Kaiserslautern, Germany 2019
University of Kaiserslautern (TU Kaiserslautern)
PhD thesis
The advent of heterogeneous many-core systems has increased the spectrum of achievable performance from multi-threaded programming. As the processor components become more distributed, the cost of synchronization and communication needed to access the shared resources increases. Concurrent linearizable access to shared objects can be prohibitively expensive in a high contention workload. Though there are various mechanisms (eg, lock-free data structures) to circumvent the synchronization overhead in linearizable objects, it still incurs performance overhead for many concurrent data types. Moreover, many applications do not require linearizable objects and apply ad-hoc techniques to eliminate synchronous atomic updates. In this thesis, we propose the Global-Local View Model. This programming model exploits the heterogeneous access latencies in many-core systems. In this model, each thread maintains different views on the shared object: a thread-local view and a global view. As the thread-local view is not shared, it can be updated without incurring synchronization costs. The local updates become visible to other threads only after the thread-local view is merged with the global view. This scheme improves the performance at the expense of linearizability.
Keywords : Scalable Consistency, Concurrent Data Types, Parallel Computing, Concurrent programming, Transactional memory, Mergeable objects, Relaxed consistency, Concurrent Data Structures, Weak Consistency
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