Kernel-assisted and Topology-aware MPI Collective Communication among Multicore or Many-core Clusters

Multicore or many-core clusters have become the most prominent form of High Performance Computing (HPC) systems. Hardware complexity and hierarchies not only exist in the inter-node layer, i.e., hierarchical networks, but also exist in internals of multicore compute nodes, e.g., Non Uniform Memory Accesses (NUMA), network-style interconnect, and memory and shared cache hierarchies. Message Passing Interface (MPI), the most widely adopted in the HPC communities, suffers from decreased performance and portability due to increased hardware complexity of multiple levels. We identified three critical issues specific to collective communication: The first problem arises from the gap between logical collective topologies and underlying hardware topologies; Second, current MPI communications lack efficient shared memory message delivering approaches; Last, on distributed memory machines, like multicore clusters, a single approach cannot encompass the extreme variations not only in the bandwidth and latency capabilities, but also in features such as the aptitude to operate multiple concurrent copies simultaneously. To bridge the gap between logical collective topologies and hardware topologies, we developed a distance-aware framework to integrate the knowledge of hardware distance into collective algorithms in order to dynamically reshape the communication patterns to suit the hardware capabilities. Based on process distance information, we used graph partitioning techniques to organize the MPI processes in a multi-level hierarchy, mapping on the hardware characteristics. Meanwhile, we took advantage of the kernel-assisted one-sided single-copy approach (KNEM) as the default shared memory delivering method. Via kernel-assisted memory copy, the collective algorithms offload copy tasks onto non-leader/not-root processes to evenly distribute copy workloads among available cores. Finally, on distributed memory machines, we developed a technique to compose multi-layered collective algorithms together to express a multi-level algorithm with tight interoperability between the levels. This tight collaboration results in more overlaps between inter- and intra-node communication. Experimental results have confirmed that, by leveraging several technologies together, such as kernel-assisted memory copy, the distance-aware framework, and collective algorithm composition, not only do MPI collectives reach the potential maximum performance on a wide variation of platforms, but they also deliver a level of performance immune to modifications of the underlying process-core binding

Efficient techniques to provide scalability for token-based cache coherence protocols

Cache coherence protocols based on tokens can provide low latency without relying on non-scalable interconnects thanks to the use of efficient requests that are unordered. However, when these unordered requests contend for the same memory block, they may cause protocols races. To resolve the races and ensure the completion of all the cache misses, token protocols use a starvation prevention mechanism that is inefficient and non-scalable in terms of required storage structures and generated traffic. Besides, token protocols use non-silent invalidations which increase the latency of write misses proportionally to the system size. All these problems make token protocols non-scalable. To overcome the main problems of token protocols and increase their scalability, we propose a new starvation prevention mechanism named Priority Requests. This mechanism resolves contention by an efficient, elegant, and flexible method based on ordered requests. Furthermore, thanks to Priority Requests, efficient techniques can be applied to limit the storage requirements of the starvation prevention mechanism, to reduce the total traffic generated for managing protocol races, and to reduce the latency of write misses. Thus, the main problems of token protocols can be solved, which, in turn, contributes to wide their efficiency and scalability.Cuesta Sáez, BA. (2009). Efficient techniques to provide scalability for token-based cache coherence protocols [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/6024Palanci

Scalable Hardware-Based Multicast Trees

c ○ 2003 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by a contractor or affiliate of the U.S. Government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only