FPGAs provide a programmable, energy-efficient, and compute-intensive acceleration substrate; thus, on the one hand, they offer a compelling solution for optimizing serverless workloads in cloud environments. On the other hand, FPGAs also introduce significant challenges that directly contradict the serverless model in the cloud, including their low-level and complex programming APIs, lack of virtualization and isolation mechanisms, high reconfiguration and communication overheads, and absence of orchestration mechanisms.

In this paper, we focus on the challenging task of reliably estimating factual knowledge that is embedded inside large language models (LLMs). To avoid reliability concerns with prior approaches, we propose to eliminate prompt engineering when probing LLMs for factual knowledge. Our approach, called Zero-Prompt Latent Knowledge Estimator (ZP-LKE), leverages the in-context learning ability of LLMs to communicate both the factual knowledge question as well as the expected answer format. Our knowledge estimator is both conceptually simpler (i.e., doesn't depend on meta-linguistic judgments of LLMs) and easier to apply (i.e., is not LLM-specific), and we demonstrate that it can surface more of the latent knowledge embedded in LLMs. We also investigate how different design choices affect the performance of ZP-LKE. Using the proposed estimator, we perform a large-scale evaluation of the factual knowledge of a variety of open-source LLMs, like OPT, Pythia, Llama(2), Mistral, Gemma, etc. over a large set of relations and facts from the Wikidata knowledge base. We observe differences in the factual knowledge between different model families and models of different sizes, that some relations are consistently better known than others but that models differ in the precise facts they know, and differences in the knowledge of base models and their finetuned counterparts. Code available at: https://github.com/QinyuanWu0710/ZeroPrompt_LKE

Introducing new (learned) features into a DBMS requires considerable experimentation and benchmarking to avoid regressions in production (customer) workloads. Using standard benchmarks such as TPC-H and TCH-DS is common practice, but, unfortunately, these do not represent the complexity of real production workloads. To solve this problem, in this demo, we propose a technique that generates a synthetic dataset from query logs and metadata—without touching the original data. The keystone of our approach is to map the data generation as a SAT problem where constraints, such as runtime cardinalities, are extracted from query logs and metadata. We show that our approach can generate representative benchmarks mirroring the performance of the original data without trading off privacy. The demo will guide the attendees through the various steps involved in the data generation and testing process.

Tesseract is the first distributed system for executing general graph mining algorithms on evolving graphs. Tesseract scales out by decomposing a stream of graph updates into per-update mining tasks and dynamically assigning these tasks to a set of distributed workers. We present a novel approach to change detection that efficiently determines the exact modifications to the algorithm's output for each update to the input graph. We use a disaggregated, multiversioned graph store to allow workers to process updates independently, without producing duplicates. Moreover, Tesseract provides interactive mining insights for complex applications using an incremental aggregation API. Finally, we implement and evaluate Tesseract and demonstrate that it achieves orders-of-magnitude improvements over state-of-the-art systems.

Distributed LSM-based databases face throughput and latency issues due to load imbalance across instances and interference from background tasks such as flushing, compaction, and data migration. Hailstorm addresses these problems by deploying the database storage engines over a distributed filesystem that disaggregates storage from processing, enabling storage pooling and compaction offloading. Hailstorm pools storage devices within a rack, allowing each storage engine to fully utilize the aggregate rack storage capacity and bandwidth. Storage pooling successfully handles load imbalance without the need for resharding. Hailstorm offloads compaction tasks to remote nodes, distributing their impact, and improving overall system throughput and response time. We show that Hailstorm achieves load balance in many MongoDB deployments with skewed workloads, improving the average throughput by 60%, while decreasing tail latency by as much as 5X. In workloads with range queries, Hailstorm provides up to 22X throughput improvements. Hailstorm also enables cost savings of 47-56% in OLTP workloads.

Current cluster computing frameworks suffer from load imbalance and limited parallelism due to skewed data distributions, processing times, and machine speeds. We observe that the underlying cause for these issues in current systems is that they partition work statically. Hurricane is a high-performance large-scale data analytics system that successfully tames skew in novel ways. Hurricane performs adaptive work partitioning based on load observed by nodes at runtime. Overloaded nodes can spawn clones of their tasks at any point during their execution, with each clone processing a subset of the original data. This allows the system to adapt to load imbalance and dynamically adjust task parallelism to gracefully handle skew. We support this design by spreading data across all nodes and allowing nodes to retrieve data in a decentralized way. The result is that Hurricane automatically balances load across tasks, ensuring fast completion times. We evaluate Hurricane's performance on typical analytics workloads and show that it significantly outperforms state-of-the-art systems for both uniform and skewed datasets, because it ensures good CPU and storage utilization in all cases.

Chaos scales graph processing from secondary storage to multiple machines in a cluster. Earlier systems that process graphs from secondary storage are restricted to a single machine, and therefore limited by the bandwidth and capacity of the storage system on a single machine. Chaos is limited only by the aggregate bandwidth and capacity of all storage devices in the entire cluster.