Bettina Kemme

A principled approach for energy-efficient training via phase-aware GPU frequency tuning

Miguel Braga

Júlio Pinto

Rahma Nouaji

Olivier Michaud

Bettina Kemme

Oana Balmau

Cláudia Brito

Ricardo Macedo

Modern AI model training imposes unprecedented computational demands, making it a key contributor to datacenter energy consumption. Yet a si… (voir plus)gnificant fraction of the energy consumed during training does not translate to useful computation due to bottlenecks throughout the training pipeline. We present PAFT, a phase-aware, dynamically adaptable GPU frequency tuning system that reduces energy consumption of training workloads with minimal performance overhead. The key insight behind PAFT is that bottlenecks represent an energy optimization opportunity, rather than purely a performance problem: when GPUs are bound to stall, PAFT opportunistically reduces their clock frequencies to match the pace of bottlenecked devices, saving energy without impacting execution time. PAFT achieves this by continuously monitoring pipeline behavior and applying fine-grained frequency adjustments, adapting to workload and system changes. Experiments conducted on twelve widely used models show that PAFT consistently outperforms all baselines, achieving energy savings of up to 46% with an average overhead of 4%.

2026-09-20

arXiv (prépublication)

doi.org

arxiv.org

A Pragmatic Approach to Learned Indexing in RocksDB: Targeted Optimizations with Minimal System Modification

Shubham Vashisth

Olivier Michaud

Bettina Kemme

Oana Balmau

Learned indexes have emerged as a promising alternative to traditional index structures, offering higher throughput and lower memory usage b… (voir plus)y approximating the cumulative key distribution function with lightweight models. Despite these benefits, adoption in production systems remains limited, partly because learned indexes that support concurrency and persistence as effectively as, e.g., the B+-Tree, do not yet exist, while many research prototypes introduce substantial complexity. In this paper, we investigate whether off-the-shelf learned indexes can be integrated into a production database with minimal storage-engine redesign. Using RocksDB as a case study, we exploit its separation between in-memory Memtables and immutable on-disk files to deploy specialized indexes at each level. We show that directly applying existing learned indexes is insufficient under write-heavy workloads because frequent Memtable replacement prevents models from fully adapting. To address this, we introduce a reuse mechanism that preserves structural knowledge across Memtable instances. At the storage level, we replace RocksDB's disk index with a learned index without modifying the storage layer or read path. We further adapt a read-only learned index to be block-aware, enabling worst-case single-I/O lookups. We implement these techniques in MountDB, an extension of RocksDB. Experiments on large-scale workloads with diverse data distributions and access patterns show up to 1.5X higher write throughput and 2.1X higher read throughput than state-of-the-art systems, demonstrating that established learned indexes can be integrated into production systems with minimal overhead and substantial performance benefits.

2026-05-21

arXiv (prépublication)

doi.org

arxiv.org

PostLearn: Towards A Learned Index For PostgreSQL

Abrar Fuad

Shubham Vashisth

Oana Balmau

Bettina Kemme

2026-04-25

Challenges and Opportunities of Efficient and Performant Storage Systems @ European Conference on Computer Systems (publié)

doi.org