GPU memory systems adopt a multi-dimensional hardware structure to provide the bandwidth necessary to support 100s to 1000s of concurrent threads. On the software side, GPU-compute workloads also use multi-dimensional structures to organize the threads. We observe that these structures can combine unfavorably and create significant resource imbalance in the memory subsystem-causing low performance and poor power-efficiency. The key issue is that it is highly applicationdependent which memory address bits exhibit high variability. To solve this problem, we first provide an entropy analysis approach tailored for the highly concurrent memory request behavior in GPU-compute workloads. Our window-based entropy metric captures the information content of each address bit of the memory requests that are likely to co-exist in the memory system at runtime. Using this metric, we find that GPU-compute workloads exhibit entropy valleys distributed throughout the lower order address bits. This indicates that efficient GPU-address mapping schemes need to harvest entropy from broad address-bit ranges and concentrate the entropy into the bits used for channel and bank selection in the memory subsystem. This insight leads us to propose the Page Address Entropy (PAE) mapping scheme which concentrates the entropy of the row, channel and bank bits of the input address into the bank and channel bits of the output address. PAE maps straightforwardly to hardware and can be implemented with a tree of XOR-gates. PAE improves performance by 1.31× and power-efficiency by 1.25× compared to state-of-the-art permutation-based address mapping.
Emerging GPU applications exhibit increasingly high computation demands which has led GPU manufacturers to build GPUs with an increasingly large number of streaming multiprocessors (SMs). Providing data to the SMs at high bandwidth puts significant pressure on the memory hierarchy and the Network-on-Chip (NoC). Current GPUs typically partition the memory-side last-level cache (LLC) in equally-sized slices that are shared by all SMs. Although a shared LLC typically results in a lower miss rate, we find that for workloads with high degrees of data sharing across SMs, a private LLC leads to a significant performance advantage because of increased bandwidth to replicated cache lines across different LLC slices.In this paper, we propose adaptive memory-side last-level GPU caching to boost performance for sharing-intensive workloads that need high bandwidth to read-only shared data. Adaptive caching leverages a lightweight performance model that balances increased LLC bandwidth against increased miss rate under private caching. In addition to improving performance for sharing-intensive workloads, adaptive caching also saves energy in a (co-designed) hierarchical two-stage crossbar NoC by power-gating and bypassing the second stage if the LLC is configured as a private cache. Our experimental results using 17 GPU workloads show that adaptive caching improves performance by 28.1% on average (up to 38.1%) compared to a shared LLC for sharing-intensive workloads. In addition, adaptive caching reduces NoC energy by 26.6% on average (up to 29.7%) and total system energy by 6.1% on average (up to 27.2%) when configured as a private cache. Finally, we demonstrate through a GPU NoC design space exploration that a hierarchical two-stage crossbar is both more power-and area-efficient than full and concentrated crossbars with the same bisection bandwidth, thus providing a low-cost cooperative solution to exploit workload sharing behavior in memory-side last-level caches. CCS CONCEPTS• Computer systems organization → Single instruction, multiple data.
The advancements in healthcare practice have brought to the fore the need for flexible access to health-related information and created an ever-growing demand for the design and the development of data management infrastructures for translational and personalized medicine. In this paper, we present the data management solution implemented for the MyHealthAvatar EU research project, a project that attempts to create a digital representation of a patient's health status. The platform is capable of aggregating several knowledge sources relevant for the provision of individualized personal services. To this end, state of the art technologies are exploited, such as ontologies to model all available information, semantic integration to enable data and query translation and a variety of linking services to allow connecting to external sources. All original information is stored in a NoSQL database for reasons of efficiency and fault tolerance. Then it is semantically uplifted through a semantic warehouse which enables efficient access to it. All different technologies are combined to create a novel web-based platform allowing seamless user interaction through APIs that support personalized, granular and secure access to the relevant information.
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