arXiv subjects

Matthieu Dorier

Publications and source records attributed to Matthieu Dorier.

5 recordsLinked to original sources

arXiv2026

StreamGuard: Low-Overhead Resilience for Real-time HPC Data Streams

Real-time scientific workflows operate on continuous data streams and must produce timely, high-quality results despite executing on complex, failure-prone infrastructure. Hardware faults, network disruptions, and performance anomalies caused by resource contention or system heterogeneity can severely degrade performance and violate real-time constraints. We focus on strengthening the resilience of the producer-consumer streaming pattern, a fundamental building block of scientific streaming workflows. We present two complementary techniques: (i) a dynamic, asynchronous, non-blocking checkpointing mechanism that preserves progress without interrupting computation, and (ii) a progress-aware load redistribution strategy that detects slow workers and proactively rebalances tasks. Together, these mechanisms maintain forward progress and balanced execution even in highly error-prone environments. Experimental results show that our approach reduces the impact of failures and performance anomalies by up to 6x, while introducing less than 1% overhead in failure-free execution.

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arXiv2026

When More Cores Hurts: The Vector Database Scaling Paradox in HPC

Vector databases have been designed and optimized for cloud environments; however, emerging scientific AI workloads (e.g., molecular search, meteorological trajectory detection, and literature-driven hypothesis generation) demand efficient, scalable execution on HPC systems. We present a large-scale evaluation of three state-of-the-art vector databases -- Qdrant, Milvus, and Weaviate -- on two production supercomputers, scaling to 256 distributed workers across 64 compute nodes. We evaluate representative workload patterns -- mixed read/write and write-then-read -- using popular benchmarks, multimodal embeddings, and a novel real-world scientific dataset. Our results reveal that workload characteristics can limit latency reduction, additional cores can reduce query throughput by up to 30.67%, and scaling from 16 to 256 workers (16x) only yields a 5.46x improvement. This scaling paradox exposes the fundamental mismatch between cloud-oriented designs and HPC systems, highlighting the need for new, HPC-aware vector database designs.

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arXiv2024

Thallus: An RDMA-based Columnar Data Transport Protocol

The volume of data generated and stored in contemporary global data centers is experiencing exponential growth. This rapid data growth necessitates efficient processing and analysis to extract valuable business insights. In distributed data processing systems, data undergoes exchanges between the compute servers that contribute significantly to the total data processing duration in adequately large clusters, necessitating efficient data transport protocols. Traditionally, data transport frameworks such as JDBC and ODBC have used TCP/IP-over-Ethernet as their underlying network protocol. Such frameworks require serializing the data into a single contiguous buffer before handing it off to the network card, primarily due to the requirement of contiguous data in TCP/IP. In OLAP use cases, this serialization process is costly for columnar data batches as it involves numerous memory copies that hurt data transport duration and overall data processing performance. We study the serialization overhead in the context of a widely-used columnar data format, Apache Arrow, and propose leveraging RDMA to transport Arrow data over Infiniband in a zero-copy manner. We design and implement Thallus, an RDMA-based columnar data transport protocol for Apache Arrow based on the Thallium framework from the Mochi ecosystem, compare it with a purely Thallium RPC-based implementation, and show substantial performance improvements can be achieved by using RDMA for columnar data transport.

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arXiv2022

HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization

Distributed data storage services tailored to specific applications have grown popular in the high-performance computing (HPC) community as a way to address I/O and storage challenges. These services offer a variety of specific interfaces, semantics, and data representations. They also expose many tuning parameters, making it difficult for their users to find the best configuration for a given workload and platform. To address this issue, we develop a novel variational-autoencoder-guided asynchronous Bayesian optimization method to tune HPC storage service parameters. Our approach uses transfer learning to leverage prior tuning results and use a dynamically updated surrogate model to explore the large parameter search space in a systematic way. We implement our approach within the DeepHyper open-source framework, and apply it to the autotuning of a high-energy physics workflow on Argonne's Theta supercomputer. We show that our transfer-learning approach enables a more than $40\times$ search speedup over random search, compared with a $2.5\times$ to $10\times$ speedup when not using transfer learning. Additionally, we show that our approach is on par with state-of-the-art autotuning frameworks in speed and outperforms them in resource utilization and parallelization capabilities.

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arXiv2018

A Cross-Layer Solution in Scientific Workflow System for Tackling Data Movement Challenge

Scientific applications in HPC environment are more com-plex and more data-intensive nowadays. Scientists usually rely on workflow system to manage the complexity: simply define multiple processing steps into a single script and let the work-flow systems compile it and schedule all tasks accordingly. Numerous workflow systems have been proposed and widely used, like Galaxy, Pegasus, Taverna, Kepler, Swift, AWE, etc., to name a few examples. Traditionally, scientific workflow systems work with parallel file systems, like Lustre, PVFS, Ceph, or other forms of remote shared storage systems. As such, the data (including the intermediate data generated during workflow execution) need to be transferred back and forth between compute nodes and storage systems, which introduces a significant performance bottleneck on I/O operations. Along with the enlarging perfor-mance gap between CPU and storage devices, this bottleneck is expected to be worse. Recently, we have introduced a new concept of Compute-on-Data-Path to allow tasks and data binding to be more efficient to reduce the data movement cost. To workflow systems, the key is to exploit the data locality in HPC storage hierarchy: if the datasets are stored in compute nodes, near the workflow tasks, then the task can directly access them with better performance with less network usage. Several recent studies have been done regarding building such a shared storage system, utilizing compute node resources, to serve HPC workflows with locality, such as Hercules [1] and WOSS [2] etc. In this research, we further argue that providing a compute-node side storage system is not sufficient to fully exploit data locality. A cross-layer solution combining storage system, compiler, and runtime is necessary. We take Swift/T [3], a workflow system for data-intensive applications, as a prototype platform to demonstrate such a cross-layer solution

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