arXiv subjects

Arthur Bernhardt

Publications and source records attributed to Arthur Bernhardt.

4 recordsLinked to original sources

arXiv2026

Bringing Data Transformations Near-Memory for Low-Latency Analytics in HTAP Environments

In this paper we propose an approach for executing data transformations near- or in-storage on intelligent storage systems. The currently prevailing approach of extracting the data and then transforming it to a target format suffers degraded performance during transformation and causes heavy data movement. Our results show robust performance of foreground workloads and lower resource contention. Our vision draws architectural opportunities in multi-engine and multi-system settings, as well as for reuse.

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arXiv2025

PUL: Pre-load in Software for Caches Wouldn't Always Play Along

Memory latencies and bandwidth are major factors, limiting system performance and scalability. Modern CPUs aim at hiding latencies by employing large caches, out-of-order execution, or complex hardware prefetchers. However, software-based prefetching exhibits higher efficiency, improving with newer CPU generations. In this paper we investigate software-based, post-Moore systems that offload operations to intelligent memories. We show that software-based prefetching has even higher potential in near-data processing settings by maximizing compute utilization through compute/IO interleaving.

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arXiv2022

bloomRF: On Performing Range-Queries in Bloom-Filters with Piecewise-Monotone Hash Functions and Prefix Hashing

We introduce bloomRF as a unified method for approximate membership testing that supports both point- and range-queries. As a first core idea, bloomRF introduces novel prefix hashing to efficiently encode range information in the hash-code of the key itself. As a second key concept, bloomRF proposes novel piecewise-monotone hash-functions that preserve local order and support fast range-lookups with fewer memory accesses. bloomRF has near-optimal space complexity and constant query complexity. Although, bloomRF is designed for integer domains, it supports floating-points, and can serve as a multi-attribute filter. The evaluation in RocksDB and in a standalone library shows that it is more efficient and outperforms existing point-range-filters by up to 4x across a range of settings and distributions, while keeping the false-positive rate low.

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arXiv2020

bloomRF: On Performing Range-Queries with Bloom-Filters based on Piecewise-Monotone Hash Functions and Dyadic Trace-Trees

We introduce bloomRF as a unified method for approximate membership testing that supports both point- and range-queries on a single data structure. bloomRF extends Bloom-Filters with range query support and may replace them. The core idea is to employ a dyadic interval scheme to determine the set of dyadic intervals covering a data point, which are then encoded and inserted. bloomRF introduces Dyadic Trace-Trees as novel data structure that represents those covering intervals implicitly. A Trace-Tree encoding scheme represents the set of covering intervals efficiently, in a compact bit representation. Furthermore, bloomRF introduces novel piecewise-monotone hash functions that are locally order-preserving and thus support range querying. We present an efficient membership computation method for range-queries. Although, bloomRF is designed for integers it also supports string and floating-point data types. It can also handle multiple attributes and serve as multi-attribute filter. We evaluate bloomRF in RocksDB and in a standalone library. bloomRF is more efficient and outperforms existing point-range-filters by up to 4x across a range of settings.

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