This study builds on the transaction cost theory to examine the performance effects of interorganizational cooperation and develops an understanding of how different forms of cooperative relationships affect organizational performance within a major service sector, namely the healthcare industry. The authors study, in particular, two cooperative forms applied to hospitals: hospital-based health networks and health systems. An empirical investigation of 382 hospitals in Taiwan shows that interorganizational cooperation exerts positive effects on hospital performance. In addition, health systems have greater positive effects on hospital efficiency than health networks. Furthermore, this study finds that the positive impact of participation in health systems is especially significant for private hospitals, local community hospitals, and hospitals in highly competitive regions. The findings reveal that performance impacts of interorganizational cooperation are contingent on the cooperative arrangements in the healthcare industry.

The growing memory demands of modern applications have driven the adoption of far memory technologies in data centers to provide cost-effective, high-capacity memory solutions. However, far memory presents new performance challenges because its access latencies are significantly longer and more variable than local DRAM. For applications to achieve acceptable performance on far memory, a high degree of memory-level parallelism (MLP) is needed to tolerate the long access latency. While modern out-of-order processors are capable of exploiting a certain degree of MLP, they are constrained by resource limitations and hardware complexity. The key obstacle is the synchronous memory access semantics of traditional load/store instructions, which occupy critical hardware resources for a long time. The longer far memory latencies exacerbate this limitation. This paper proposes a set of Asynchronous Memory Access Instructions (AMI) and its supporting function unit, Asynchronous Memory Access Unit (AMU), inside contemporary Out-of-Order Core. AMI separates memory request issuing from response handling to reduce resource occupation. Additionally, AMU architecture supports up to several hundreds of asynchronous memory requests through re-purposing a portion of L2 Cache as scratchpad memory (SPM) to provide sufficient temporal storage. Together with a coroutine-based programming framework, this scheme can achieve significantly higher MLP for hiding far memory latencies. Evaluation with a cycle-accurate simulation shows AMI achieves 2.42 × speedup on average for memory-bound benchmarks with 1μs additional far memory latency. Over 130 outstanding requests are supported with 26.86 × speedup for GUPS (random access) with 5 μs latency. These demonstrate how the techniques tackle far memory performance impacts through explicit MLP expression and latency adaptation.

Frame refreshes, that are used to retain frame images from frame buffers for display subsystems in mobile devices, waste energy and memory bandwidth. In this paper, we propose an intelligent frame refresh mechanism to reduce redundant frame refreshes and useless data accesses to frame buffers, which bridges the semantic gap between frame buffers and frame refreshes, and exploits the knowledge of frame buffers to guide frame refreshes. Based on this mechanism, we introduce two detailed schemes to optimize refreshes by utilizing different information. The flipping-aware frame refresh scheme uses the frame buffer switching operations to detect frame image updates and triggers useful refreshes. The row-level frame refresh scheme supports to refresh only modified rows instead of the whole frame, under the guidance of pixel status information of frame buffers. Our evaluation results show that our proposed mechanism can reduce memory requests by nearly 50% and memory power consumption up to 30%, compared to conventional fixed frame refresh mechanism.

Recent advances in high-speed mobile networks have revealed new bottlenecks in ubiquitous TCP protocol deployed in the Internet. In addition to differentiating non-congestive loss from congestive loss, our experiments revealed two significant performance bottlenecks during the loss recovery phase: flow control bottleneck and application stall, resulting in degradation in QoS performance. To tackle these two problems, we firstly develop a novel opportunistic retransmission algorithm to eliminate the flow control bottleneck, which enables TCP sender to transmit new packets even if receiver's receiving window is exhausted. Secondly, application stall can be significantly alleviated by carefully monitoring and tuning the TCP sending buffer growth mechanism. We implemented and modularized the proposed algorithms in the Linux kernel thus they can plug-and-play with the existing TCP loss recovery algorithms easily. We evaluated our proposed algorithms over emulated and real experiments and showed that, compared to existing TCP loss recovery algorithms, the proposed optimization algorithms improve the bandwidth efficiency by up to 133% and completely mitigate RTT spikes, i.e., over 50% RTT reduction, over the loss recovery phase.