Fast and Accurate Entity Linking via Graph Embedding.

Unified and Scalable Incremental Recommenders with Consumed Item Packs.

Private Federated Learning with Domain Adaptation.

FEEDBACK-DIRECTED STATIC ANALYSIS

A persistent lock-free queue for non-volatile memory.

RAPID - In-Memory Analytical Query Processing Engine with Extreme Performance per Watt.

STAGED DYNAMIC TAINT FLOW INFERENCE

Brief Announcement: Persistent Multi-Word Compare-and-Swap

This brief announcement presents a fundamental concurrent primitive for persistent memory – a persistent atomic multi-word compare-and-swap (PMCAS).We present a novel algorithm carefully crafted to ensure that atomic updates to a multitude of words modified by the PMCAS are persisted correctly. Our algorithm leverages hardware transactional memory (HTM) for concurrency control, and has a total of 3 persist barriers in its critical path. We also overview variants based on just the compare-and-swap (CAS) instruction and a hybrid of CAS and HTM.

A Two-List Framework for Accurate Detection of Frequent Items in Data Streams

The problem of detecting the most frequent items in large data sets and providing accurate frequency estimates for those items is becoming more and more important in a variety of domains. We propose a new two-list framework for addressing this problem, which extends the state-of-the-art Filtered Space-Saving (FSS) algorithm. An algorithm called FSSA giving an efficient array-based implementation of this framework is presented. An adaptive version of this algorithm is also presented, which adjusts the relative sizes of the two lists based on the estimated number of distinct keys in the data set. Analytical comparison with the FSS algorithm showed that FSSA has smaller expected frequency esti-mation errors, and experiments on both artificial and real workloads confirm this result. A theoretical analysis of space and time complexity for FSSA and its benchmark algorithms was performed. Finally, we showed that FSS2L frame-work can be naturally parallelized, leading to a linear decrease in the maximum frequency estimation error.

Closing the Performance Gap Between Volatile and Persistent Key-Value Stores Using Cross-Referencing Logs

Key-Value (K-V) stores are an integral building block of modern datacenter applications. With byte addressable persistent memory (PM) technologies, such as Intel/Micron’s 3D XPoint, on the horizon, there has been an influx of new high performance K-V stores that leverage PM for performance. However, there remains a significant performance gap between PM optimized K-V stores and DRAM resident ones, largely reflecting the gap between projected PM latency relative to that of DRAM. We address that performance gap with Bullet, a K-V store that leverages both the byte-addressability of PM and the lower latency of DRAM, using a technique called cross-referencing logs (CRLs) to keep most PM updates off the critical path. Bullet delivers performance approaching that of DRAM resident K-V stores by maintaining two hash tables, one in the slower (backend) PM and the other in the faster (frontend) DRAM. CRLs are a scalable persistent logging mechanism that keeps the two copies mutually consistent. Bullet also incorporates several critical optimizations, such as dynamic load balancing between frontend and backend threads, support for nonblocking Gets, and opportunistic omission of stale updates in the backend. This combination of implementation techniques delivers performance within 5% of that of DRAM-only key-value stores for realistic (read-heavy) workloads. Our general approach, based on CRLs, is “universal” in that it can be used to turn any volatile K-V store into a persistent one (or vice-versa, provide a fast cache for a persistent K-V store).

PerfIso: Performance Isolation for Commercial Latency-Sensitive Services

Placement of Virtual Containers on NUMA systems: A Practical and Comprehensive Model

Our work addresses the problem of placement of threads, or virtual cores, onto physical cores in a multicore NUMA system. Different placements result in varying degrees of contention for shared resources, so choosing the right placement can have a large effect on performance. Prior work has studied this problem, but either addressed hardware with specific properties, leaving us unable to generalize the models to other systems, or modeled much simpler effects than the actual performance in different placements. Our contribution is a general framework for reasoning about workload placement on machines with shared resources. It enables us to build an accurate performance model for any machine with a hierarchy of known shared resources automatically, with only minimal input from the user. Using our methodology, data center operators can minimize the number of NUMA (CPU+memory) nodes allocated for an application or a service, while ensuring that it meets performance objectives.

PerfIso: Performance Isolation for Commercial Latency-Sensitive Services

Large commercial latency-sensitive services, such as web search, run on dedicated clusters provisioned for peak load to ensure responsiveness and tolerate data center outages. As a result, the average load is far lower than the peak load used for provisioning, leading to resource under-utilization. The idle resources can be used to run batch jobs, completing useful work and reducing overall data center provisioning costs. However, this is challenging in practice due to the complexity and stringent tail-latency requirements of latency-sensitive services. Left unmanaged, the competition for machine resources can lead to severe response-time degradation and unmet service-level objectives (SLOs). This work describes PerfIso, a performance isolation framework which has been used for nearly three years in Microsoft Bing, a major search engine, to colocate batch jobs with production latency-sensitive services on over 90,000 servers. We discuss the design and implementation of PerfIso, and conduct an experimental evaluation in a production environment. We show that colocating CPU-intensive jobs with latency-sensitive services increases average CPU utilization from 21% to 66% for off-peak load without impacting tail latency.

MTBase - Optimizing Cross-Tenant Database Queries.

AFFOGATO - runtime detection of injection attacks for Node.js.