arXiv subjects

Helena Caminal

Publications and source records attributed to Helena Caminal.

5 recordsLinked to original sources

arXiv2026

The Case for TPU-Accelerated Analytical Query Processing

Large-scale analytical query processing requires computing substrates capable of immense distributed scale-out. Tensor Processing Units (TPUs) perfectly embody this extreme-scale architecture, offering massive computational density and highly scalable optical pod-level interconnects. Despite these advantages, TPUs have so far remained largely unexplored for analytical database workloads. In this paper, we present the first comprehensive case for TPU-accelerated analytical query processing by running all the TPC-H queries entirely on the TPUs. We introduce Native TPU Accelerator (NTA) for analytical query processing, which provides hardware-conscious algorithm design and optimizations in JAX/XLA. By exposing the TPU's structural mechanics, we demonstrate how dynamic, memory-bound database operations can be rewritten into deterministic JAX programs. We engineer a suite of novel execution strategies that leverage both the TPU's TensorCore and SparseCore to trade sparse, unpredictable memory accesses for dense, hardware-aligned ones. At the core of NTA, a compiler uses catalog statistics to assign guarded static capacities and a hardware-calibrated local cost model to select a TPU-native implementation for each relational operator before JIT compilation. We evaluate our system using the TPC-H benchmark. Taking full advantage of TPU slices and JAX distributed computing, we seamlessly scale our execution environment to run TPC-H benchmark up to Scale Factor 1000 (SF1000). Evaluated against a rigorous CPU ground truth (DuckDB), our native TPU accelerator achieves state-of-the-art performance, achieving a 7.0x single-device speedup over DuckDB at SF100 and a 68.6x speedup with 16 TPUs at SF1000 (1TB). The results establish TPUs not merely as dense neural compute hardware, but as a highly competitive, massively scalable substrate for the next generation of analytical query processing.

cs.DB↗

arXiv2026

VADER: Filtered Vector Search with Declarative Recall

Approximate filtered vector search (FVS), a core operation in many data management tasks that combine structured data with vector embeddings, exhibits increased complexity due to the characteristics of filtering predicates. Each predicate is defined by selectivity (i.e., the fraction of vectors that satisfy the predicate) and correlation (i.e., the relationship between the filter and the vector space), which can significantly affect search difficulty even for the same query vector. This poses a key challenge for users aiming to integrate vector search with structured data, as efficient execution often requires extensive manual tuning of algorithm parameters. In this paper, we present VADER, the first approach that eliminates hyperparameter tuning by introducing declarative recall for approximate filtered vector search. With declarative recall, users specify a desired recall target, and VADER executes FVS queries to meet this target without requiring manual configuration. VADER achieves this by employing a filter-aware recall predictor that generalizes across varying selectivities and correlations without explicit tuning, and by performing early termination once the predicted recall reaches the user-defined target. Through extensive experimental evaluation, we show that VADER achieves near-optimal early termination, while providing significant speedups of up to 53% faster and improved result quality of 28% compared to the best-performing baseline.

cs.DB↗

arXiv2026

AI Query Compilation for Unified and Optimized Execution

In this vision paper, we propose a novel architectural paradigm for accelerated AI query execution via a unified compiled execution strategy. By compiling the hybrid AI Query as a whole -- integrating both standard SQL relational constructs and LLM inference layers into a single, unified tensor compute graph -- we completely alleviate PCIe data movement bottlenecks across execution boundaries and enable global compiler optimizations and efficient automatic sharding. We demonstrate the viability of this unified execution paradigm on select and extended AI queries on SemBench Reviews and Movies datasets, achieving up to 5.3x latency speedup and 9.8x throughput speedup on TPUs, and outline a research roadmap of open technical challenges to realize this vision.

cs.DB↗

arXiv2026

An In-Depth Study of Filter-Agnostic Vector Search on a PostgreSQL Database System: [Experiments and Analysis]

Filtered Vector Search (FVS) is critical for supporting semantic search and GenAI applications in modern database systems. However, existing research most often evaluates algorithms in specialized libraries, making optimistic assumptions that do not align with enterprise-grade database systems. Our work challenges this premise by demonstrating that in a production-grade database system, commonly made assumptions do not hold, leading to performance characteristics and algorithmic trade-offs that are fundamentally different from those observed in isolated library settings. This paper presents the first in-depth analysis of filter-agnostic FVS algorithms within a production PostgreSQL-compatible system. We systematically evaluate post-filtering and inline-filtering strategies across a wide range of selectivities and correlations. Our central finding is that the optimal algorithm is not dictated by the cost of distance computations alone, but that system-level overheads that come from both distance computations and filter operations (like page accesses and data retrieval) play a significant role. We demonstrate that graph-based approaches (such as NaviX/ACORN) can incur prohibitive numbers of filter checks and system-level overheads, compared with clustering-based indexes such as ScaNN, often canceling out their theoretical benefits in real-world database environments. Ultimately, our findings provide the database community with crucial insights and practical guidelines, demonstrating that the optimal choice for a filter-agnostic FVS algorithm is not absolute, but rather a system-aware decision contingent on the interplay between workload characteristics and the underlying costs of data access in a real-world database architecture.

cs.DB↗

arXiv2025

Membrane: Accelerating Database Analytics with Bank-Level DRAM-PIM Filtering

In-memory database query processing frequently involves substantial data transfers between the CPU and memory, leading to inefficiencies due to Von Neumann bottleneck. Processing-in-Memory (PIM) architectures offer a viable solution to alleviate this bottleneck. In our study, we employ a commonly used software approach that streamlines JOIN operations into simpler selection or filtering tasks using pre-join denormalization which makes query processing workload more amenable to PIM acceleration. This research explores DRAM design landscape to evaluate how effectively these filtering tasks can be efficiently executed across DRAM hierarchy and their effect on overall application speedup. We also find that operations such as aggregates are more suitably executed on the CPU rather than PIM. Thus, we propose a cooperative query processing framework that capitalizes on both CPU and PIM strengths, where (i) the DRAM-based PIM block, with its massive parallelism, supports scan operations while (ii) CPU, with its flexible architecture, supports the rest of query execution. This allows us to utilize both PIM and CPU where appropriate and prevent dramatic changes to the overall system architecture. With these minimal modifications, our methodology enables us to faithfully perform end-to-end performance evaluations using established analytics benchmarks such as TPCH and star-schema benchmark (SSB). Our findings show that this novel mapping approach improves performance, delivering a 5.92x/6.5x speedup compared to a traditional schema and 3.03-4.05x speedup compared to a denormalized schema with 9-17% memory overhead, depending on the degree of partial denormalization. Further, we provide insights into query selectivity, memory overheads, and software optimizations in the context of PIM-based filtering, which better explain the behavior and performance of these systems across the benchmarks.

cs.AR↗