GPU hash tables are increasingly used to accelerate data processing, but their limited functionality restricts adoption in large-scale data processing applications. Current limitations include incomplete concurrency support and missing compound operations such as upserts. This paper presents WarpSpeed, a library of high-performance concurrent GPU hash tables with a unified benchmarking framework for performance analysis. WarpSpeed implements eight state-of-the-art Nvidia GPU hash table designs and provides a rich API designed for modern GPU applications. Our evaluation uses diverse benchmarks to assess both correctness and scalability, and we demonstrate real-world impact by integrating these hash tables into three downstream applications. We propose several optimization techniques to reduce concurrency overhead, including fingerprint-based metadata to minimize cache line probes and specialized Nvidia GPU instructions for lock-free queries. Our findings provide new insights into concurrent GPU hash table design and offer practical guidance for developing efficient, scalable data structures on modern GPUs.
Cuckoo filters are space-efficient approximate set membership data structures with a controllable false positive rate (FPR) and zero false negatives, similar to Bloom filters. In contrast to Bloom filters, Cuckoo filters store multi-bit fingerprints of keys in a hash table using variants of Cuckoo hashing, allowing each fingerprint to be stored at a small number of possible locations. Existing Cuckoo filters use fingerprints of (k+3) bits per key and an additional space overhead factor of at least 1.05 to achieve an FPR of 2^-k. For k=10, this amounts to 1.365 kn bits to store n keys, which is better than 1.443 kn bits for Bloom filters. The +3 for the fingerprint size is required to balance out the multiplied FPR caused by looking for the fingerprint at several locations. In the original Cuckoo filter, the number of hash table buckets is restricted to a power of 2, which may lead to much larger space overheads, up to 2.1 (1+3/k) kn bits. We present two improvements of Cuckoo filters. First, we remove the restriction that the number of buckets must be a power of 2 by using a different placement strategy. Second, we reduce the space overhead factor of Cuckoo filters to 1.06 (1+2/k) by using overlapping windows instead of disjoint buckets to maintain the load threshold of the hash table, while reducing the number of alternative slots where any fingerprint may be found. A detailed evaluation demonstrates that the alternative memory layout based on overlapping windows decreases the size of Cuckoo filters not only in theory, but also in practice. A comparison with other state-of-the art filter types, Prefix filters and Vector Quotient filters (VQFs), shows that the reduced space overhead makes windowed Cuckoo filters the smallest filters supporting online insertions, with similarly fast queries, but longer insertion times.
Graph augmentation is a fundamental and well-studied problem that arises in network optimization. We consider a new variant of this model motivated by reconfigurable communication networks. In this variant, we consider a given physical network and the measured communication demands between the nodes. Our goal is to augment the given physical network with a matching, so that the shortest path lengths in the augmented network, weighted with the demands, are minimal.We prove that this problem is NP-hard, even if the physical network is a cycle. We then use results from demand-aware network design to provide a constant-factor approximation algorithm for adding a matching in case that only a few nodes in the network cause almost all the communication. For general real-world communication patterns, we design and evaluate a series of heuristics that can deal with arbitrary graphs as the underlying network structure. Our algorithms are validated experimentally using real-world traces (from e.g., Facebook) of data centers.
