Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms. However, existing approaches often use out-of-the-box sequence models which are limited by speed and memory consumption, are often infeasible for production environments, and usually do not incorporate cross-session information, which is crucial for effective recommendations. Here we propose Hierarchical Temporal Convolutional Networks (HierTCN), a hierarchical deep learning architecture that makes dynamic recommendations based on users' sequential multi-session interactions with items. HierTCN is designed for web-scale systems with billions of items and hundreds of millions of users. It consists of two levels of models: The high-level model uses Recurrent Neural Networks (RNN) to aggregate users' evolving long-term interests across different sessions, while the low-level model is implemented with Temporal Convolutional Networks (TCN), utilizing both the long-term interests and the short-term interactions within sessions to predict the next interaction. We conduct extensive experiments on a public XING dataset and a large-scale Pinterest dataset that contains 6 million users with 1.6 billion interactions. We show that HierTCN is 2.5x faster than RNN-based models and uses 90% less data memory compared to TCN-based models. We further develop an effective data caching scheme and a queue-based mini-batch generator, enabling our model to be trained within 24 hours on a single GPU. Our model consistently outperforms state-of-the-art dynamic recommendation methods, with up to 18% improvement in recall and 10% in mean reciprocal rank.

pdf/hierarchical-temporal-convolutional-networks-for-dynamic-53fljiq50d.pdf

Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems

As the number of cores per machine increases, memory architectures are being redesigned to avoid bus contention and sustain higher throughput needs. The emergence of Non-Uniform Memory Access (NUMA) constraints has caused affinities between threads and buffers to become an important decision criterion for schedulers.

/pdf/enabling-high-performance-memory-migration-for-multithreaded-3pv6gepeu2.pdf

Enabling high-performance memory migration for multithreaded applications on LINUX

To harness a heterogeneous memory hierarchy, it is advantageous to integrate application knowledge in guiding frequent memory move, i.e., replicating or migrating virtual memory regions. To this end, we present memif, a protected OS service for asynchronous, hardware-accelerated memory move. Compared to the state of the art -- page migration in Linux, memif incurs low overhead and low latency; in order to do so, it not only redefines the semantics of kernel interface but also overhauls the underlying mechanisms, including request/completion management, race handling, and DMA engine configuration. We implement memif in Linux for a server-class system-on-chip that features heterogeneous memories. Compared to the current Linux page migration, memif reduces CPU usage by up to 15% for small pages and by up to 38x for large pages; in continuously serving requests, memif has no need for request batching and reduces latency by up to 63%. By crafting a small runtime atop memif, we improve the throughputs for a set of streaming workloads by up to 33%. Overall, memif has opened the door to software management of heterogeneous memory.

memif: Towards Programming Heterogeneous Memory Asynchronously

Continuing improvements in the scale of many-core platforms are accompanied by increased asymmetry in their memory architectures. Such NUMA architectures, however, require systems software that understands this asymmetry to attain high levels of performance, leading to significant work in optimizing operating systems like Linux and Windows to increase locality of access to memory nodes and to consider differences in access latencies when accessing remote nodes. When running on today's virtualized hardware platforms, however, virtual machines (VMs) remain unaware of underlying memory architecture, leading to non-compliance with their operating system's (OS) policies for managing NUMA memory, and thereby, incurring undesirable performance overheads. This paper describes the vNUMA-mgr approach of dealing with the NUMA memory characteristics of future virtualized multicore architectures, which (1) properly manages VM memory at the hyper-visor (VMM) level through a set of VMM-level strategies, coupled with (2) 'enlightening' guest VM OSes, if possible, to aid their memory management, and also (3) provides mechanisms to maintain the distribution of VM memory (across physical nodes), even when memory is over-provisioned. The vNUMA-mgr implementation in the Xen VMM is evaluated with respect to each of its memory allocation strategies. The evaluation, which uses a set of memory-intensive benchmarks representative of many High-Performance Computing (HPC) applications, shows 30–50% performance improvement on our platform.

vNUMA-mgr: Managing VM memory on NUMA platforms

Memory seems to be so simple. If you need it then just get some and use it. On most systems, one memory page is as good as another. On a NUMA system however the distance of the memory to the executing process matters. Performance can sink dramatically if memory references are made too frequently to pages on remote nodes. Local memory is special to the execution context because it has minimal latency and optimal bandwidth characteristics.

/pdf/local-and-remote-memory-memory-in-a-linux-numa-system-44vogdl4zb.pdf

Local and Remote Memory: Memory in a Linux/NUMA System

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Local and Remote Memory: Memory in a Linux/NUMA System