Online transaction processing (OLTP) is widely used on modern cloud infrastructures to complete important businesses such as payments and stock exchanges. Remote Direct Memory Access (RDMA) is a technology that enables ultra-low inter-server memory access latency - which is critical for implementing high-performance concurrency control protocols in distributed OLTP. In this article, we develop RCC, the first unified and comprehensive RDMA-enabled distributed transaction processing framework containing six serializable concurrency control protocols—not only the classical protocols${{\sf NOWAIT}}$,${{\sf WAITDIE}}$,${{\sf OCC}}$, but also more advanced${{\sf MVCC}}$,${{\sf SUNDIAL}}$, and${{\sf CALVIN}}$— the deterministic protocol. Our goal is to unbiasedly compare protocols on OLTP workloads in a common execution environment with the concurrency control protocol being the only changeable component. From RCC, we obtained new insights on building RDMA-based protocols. We analyzed stage-wise latency breakdown to develop more efficient hybrid implementations. Moreover, RCC can enumerate all stage-wise hybrid designs under a given workload characteristic. Our results show that throughput-wise hybrid designs are better than RPC or one-sided counterparts by 32.2% and up to 67%; three hybrid designs are better than their pure counterparts by up to 17.8%. RCC can provide performance insights and be used as the common open-sourced infrastructure for fast prototyping new implementations.

Processing-In-Memory (PIM) architectures based on recent technology advances (e.g., Hybrid Memory Cube) demonstrate great potential for graph processing. However, existing solutions did not address the key challenge of graph processing---irregular data movements.

Processing-In-Memory (PIM) is an effective technique that reduces data movements by integrating processing units within memory. The recent advance of “big data” and 3D stacking technology make PIM a practical and viable solution for the modern data processing workloads. It is exemplified by the recent research interests on PIM-based acceleration. Among them, TESSERACT is a PIM-enabled parallel graph processing architecture based on Micron's Hybrid Memory Cube (HMC), one of the most prominent 3D-stacked memory technologies. It implements a Pregel-like vertex-centric programming model, so that users could develop programs in the familiar interface while taking advantage of PIM. Despite the orders of magnitude speedup compared to DRAM-based systems, TESSERACT generates excessive crosscube communications through SerDes links, whose bandwidth is much less than the aggregated local bandwidth of HMCs. Our investigation indicates that this is because of the restricted data organization required by the vertex programming model. In this paper, we argue that a PIM-based graph processing system should take data organization as a first-order design consideration. Following this principle, we propose GraphP, a novel HMC-based software/hardware co-designed graph processing system that drastically reduces communication and energy consumption compared to TESSERACT. GraphP features three key techniques. 1) “Source-cut” partitioning, which fundamentally changes the cross-cube communication from one remote put per cross-cube edge to one update per replica. 2) “Two-phase Vertex Program”, a programming model designed for the “source-cut” partitioning with two operations: GenUpdate and ApplyUpdate. 3) Hierarchical communication and overlapping, which further improves performance with unique opportunities offered by the proposed partitioning and programming model. We evaluate GraphP using a cycle accurate simulator with 5 real-world graphs and 4 algorithms. The results show that it provides on average 1.7 speedup and 89% energy saving compared to TESSERACT.

Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performance while maintaining accuracy. For DNNs, the model size is an important factor affecting performance, scalability and energy efficiency. Weight pruning achieves good compression ratios but suffers from three drawbacks: 1) the irregular network structure after pruning, which affects performance and throughput; 2) the increased training complexity; and 3) the lack of rigirous guarantee of compression ratio and inference accuracy.