Abstract
Processors with 100s of threads of execution are among the state-of-the-art in high-end computing systems. This transition to many-core computing has required the community to develop new algorithms to overcome significant latency bottlenecks through massive concurrency. However, implementing efficient parallel runtimes that can scale up to high concurrency levels with extremely fine-grained tasks remains a challenge. Existing techniques do not scale to a large number of threads due to the high cost of synchronization in concurrent data structures. We present a thorough analysis of various synchronization mechanisms including mutex, semaphore, spinlock and atomic fetch-and-add that are typically used to build concurrent data structures in task-parallel runtime systems. To overcome these limitations, in a recent work we proposed XQueue, a novel lock-less concurrent queuing system with relaxed ordering semantics that is geared towards realizing scalability up to hundreds of concurrent threads. In this work, we extend XQueue and present X-OpenMP, a library for enabling extremely fine-grained parallelism on modern many-core systems with hundreds of cores. Work stealing is a popular choice for load balancing in task-based runtime systems as it efficiently distributes the load across worker threads; however, traditional approaches rely on synchronization primitives and thus work stealing can incur overheads. Here we implement a lock-less algorithm for work stealing for total-store order (TSO) memory architectures and evaluate the performance using micro and macro benchmarks. We compare the performance of X-OpenMP with native LLVM OpenMP, GNU OpenMP, OpenCilk and oneTBB implementations using task-based linear algebra routines from PLASMA numerical library, Strassen’s matrix multiplication from the BOTS Benchmark Suite, and the Unbalanced Tree Search benchmark. Applications parallelized using OpenMP can run without modification by simply linking against the X-OpenMP library. X-OpenMP achieves up to 40X speedup compared to GNU OpenMP, up to 2X speedup compared to the native LLVM OpenMP, up to 6X speedup compared to OpenCilk and up to 5X speedup compared to oneTBB implementations. The tasking overheads in X-OpenMP are reduced by 50% compared to the native LLVM OpenMP.
Published Version
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