Persistent memory (PM) is fundamentally changing the way database index structures are built by enabling persistence, high performance, and (near) instant recovery all on the memory bus. Prior work has proposed many techniques to tailor index structure designs for PM, but they were mostly based on volatile DRAM with simulation due to the lack of real PM hardware. Until today is it unclear how these techniques will actually perform on real PM hardware. With the recent released Intel Optane DC Persistent Memory, for the first time, this paper provides a comprehensive evaluation of recent persistent index structures. We focus on B + -Tree-based range indexes and carefully choose four representative index structures for evaluation: wBTree, NV-Tree, BzTree and FPTree. These four tree structures cover a wide, representative range of techniques that are essential building blocks of PM-based index structures. For fair comparison, we used an unified programming model for all trees and developed PiBench , a benchmarking framework which targets PM-based indexes. Through empirical evaluation using representative workloads, we identify key, effective techniques, insights and caveats to guide the making of future PM-based index structures.

A new multi-illuminant synthetic image test set called MIST is described and made publicly available. MIST is intended primarily for evaluating illumination estimation and color constancy methods, but additional data are provided to make it useful for other computer vision applications as well. MIST addresses the problem found in most existing real-image datasets, which is that the groundtruth illumination is only measured at a very limited number of locations, despite the fact that illumination tends to vary significantly in almost all scenes. In contrast, MIST provides for each pixel: (a) the percent surface spectral reflectance, (b) the spectrum of the incident illumination, (c) the separate specular and diffuse components of the reflected light, and (d) the depth (ie, camera-to-surface distance). The dataset contains 900 stereo pairs, each of the 1800 images being a 30-band multispectral image covering the visible spectrum from 400 to 695 nm at a 5 nm interval. Standard sRGB versions of the multispectral images are also provided. The images are synthesized by extending the Blender Cycles ray-tracing renderer. The rendering is done in a way that ensures the images are not only photorealistic, but physically accurate as well.

A new image test set of synthetically generated, full-spectrum images with pixelwise ground truth has been developed to aid in the evaluation of illumination estimation methods for colour constancy. The performance of 9 illumination methods is reported for this dataset along and compared to the optimal single-illuminant estimate. None of the methods specifically designed to handle multi-illuminant scenes is found to perform any better than the optimal single-illuminant case based on completely uniform illumination.

The emergence of persistent memory (PM), such as Intel Optane DC Persistent Memory Modules (DCPMM), opened up many opportunities for building high-performance indexes directly on PM. However, the many PM indexes proposed by prior work had their evaluation based on PM emulation using DRAM and therefore it was not clear how they would perform on real PM hardware. Moreover, they typically used ad hoc, in-house benchmarks and did not collect PM-specific hardware metrics that are key performance indicators and are instrumental for users and developers to understand the performance behavior of PM indexes. These issues call for a systematic, fair and reproducible approach for evaluating PM indexes. This demonstration highlights the principles and lessons learned from our recent evaluation of PM indexes on real DCPMM and showcases PiBench, a unified benchmarking framework that enables fair and reproducible evaluation of PM indexes. In addition to common metrics, PiBench uniquely integrates monitoring tools to collect PM-specific hardware counters, allowing in-depth performance analysis. Our demonstration is enabled by PiBench Online, a new interactive system built on top of PiBench. Using PiBench Online, users can upload their own index implementations, run preset or customized workloads, and analyze results interactively, all through an easy-to-use web interface. PiBench is open-source and PiBench Online is deployed at https://pibench.org. We hope PiBench Online can promote fair comparison and reproducibility in database and systems communities.

Byte-addressable persistent memory (PM) brings hash tables the potential of low latency, cheap persistence and instant recovery. The recent advent of Intel Optane DC Persistent Memory Modules (DCPMM) further accelerates this trend. Many new hash table designs have been proposed, but most of them were based on emulation and perform sub-optimally on real PM. They were also piecewise and partial solutions that side-stepped many important properties, in particular good scalability, high load factor and instant recovery.