I/O performance explainability in HPC requires contextual characterization across the full software and system stack. Contextual characterization identifies the semantics and runtime role of I/O functions. State-of-the-art contextual characterization is still largely manual, but remains highly valuable for explaining bottlenecks and guiding optimization. However, manual contextual characterization is difficult to scale and hard to reproduce as I/O libraries and cross-layer interactions grow in complexity. We present Metis, a framework for systematic characterization of HPC I/O functions that uses agentic LLMs to integrate heterogeneous evidence sources and quantify agents agreement. Across evaluation, Metis improves held-out-category generalization over an MCP Tool baseline (0.90 vs. 0.35), reduces runtime (27.7 s vs. 84.5 s) while increasing throughput (41.8 vs. 14.28 functions/min), and sustains high verifier throughput under federated scaling (330K–1.18M functions/s). These results demonstrate that Metis is an effective and practical approach for explainable characterization of complex HDF5 behavior, enabling more trustworthy and reproducible HPC I/O analysis.

International audience

Scientific workflows increasingly need to train a DNN model in real-time during an experiment (e.g. using ground truth from a simulation), while using it at the same time for inferences. Instead of sharing the same model instance, the training (producer) and inference server (consumer) often use different model replicas that are kept synchronized. In addition to efficient I/O techniques to keep the model replica of the producer and consumer synchronized, there is another important trade-off: frequent model updates enhance inference quality but may slow down training; infrequent updates may lead to less precise inference results. To address these challenges, we introduce Viper: a new I/O framework designed to determine a near-optimal checkpoint schedule and accelerate the delivery of the latest model updates. Viper builds an inference performance predictor to identify the optimal checkpoint schedule to balance the trade-off between training slowdown and inference quality improvement. It also creates a memory-first model transfer engine to accelerate model delivery through direct memory-to-memory communication. Our experiments show that Viper can reduce the model update latency by ≈ 9x using the GPU-to-GPU data transfer engine and ≈ 3x using the DRAM-to-DRAM host data transfer. The checkpoint schedule obtained from Viper’s predictor also demonstrates improved cumulative inference accuracy compared to the baseline of epoch-based solutions.

I/O operations are a known performance bottleneck of HPC applications. To achieve good performance, users often employ an iterative multistage tuning process to find an optimal I/O stack configuration. However, an I/O stack contains multiple layers, such as high-level I/O libraries, I/O middleware, and parallel file systems, and each layer has many parameters. These parameters and layers are entangled and influenced by each other. The tuning process is time-consuming and complex. In this work, we present TunIO, an AI-powered I/O tuning framework that implements several techniques to balance the tuning cost and performance gain, including tuning the high-impact parameters first. Furthermore, TunIO analyzes the application source code to extract its I/O kernel while retaining all statements necessary to perform I/O. It utilizes a smart selection of high-impact configuration parameters of the given tuning objective. Finally, it uses a novel Reinforcement Learning (RL)-driven early stopping mechanism to balance the cost and performance gain. Experimental results show that TunIO leads to a reduction of up to ≈73% in tuning time while achieving the same performance gain when compared to H5Tuner. It achieves a significant performance gain/cost of 208.4 MBps/min (I/O bandwidth for each minute spent in tuning) over existing approaches under our testing.

Storage in HPC is typically a single Remote and Static Storage (RSS) resource. However, applications demonstrate diverse I/O requirements that can be better served by a multi-storage approach. Current practice employs ephemeral storage systems running on either node-local or shared storage resources. Yet, the burden of provisioning and configuring intermediate storage falls solely on the users, while global job schedulers offer little to no support for custom deployments. This lack of support often leads to over- or under-provisioning of resources and poorly configured storage systems. To mitigate this, we present LuxIO, an intelligent storage resource provisioning and auto-configuration service. LuxIO constructs storage deployments configured to best match I/O requirements. LuxIO-tuned storage services show performance improvements up to 2× across common applications and benchmarks, while introducing minimal overhead of 93.40 ms on top of existing job scheduling pipelines. LuxIO improves resource utilization by up to 25% in select workflows.

Modern applications are highly data-intensive, leading to the well-known I/O bottleneck problem. Scientists have proposed the placement of fast intermediate storage resources which aim to mask the I/O penalties. To manage these resources, three core software abstractions are being used in leadership-class computing facilities: IO Forwarders, Burst Buffers, and Data Stagers. Yet, with the rise of multi-tenant deployment in HPC systems, these software abstractions are: managed and maintained in isolation, leading to inefficient interactions; allocated statically, leading to load imbalance; exclusively bifurcated between the intermediate storage, leading to under-utilization of resources, and, in many cases, do not support in-situ operations. To this end, we present HFlow, a new class of data forwarding system that leverages a real-time data movement paradigm. HFlow introduces a unified data movement abstraction (the ByteFlow) providing data-independent tasks that can be executed anywhere and thus, enabling dynamic resource provisioning. Moreover, the processing elements executing the ByteFlows are designed to be ephemeral and, hence, enable elastic management of intermediate storage resources. Our results show that applications running under HFlow display an increase in performance of 3x when compared with state-of-the-art software solutions.

Applications and middleware services, such as data placement engines, I/O scheduling, and prefetching engines, require low-latency access to telemetry data in order to make optimal decisions. However, typical monitoring services store their telemetry data in a database in order to allow applications to query them, resulting in significant latency penalties. This work presents Apollo: a low-latency monitoring service that aims to provide applications and middleware libraries with direct access to relational telemetry data. Monitoring the system can create interference and overhead, slowing down raw performance of the resources for the job. However, having a current view of the system can aid middleware services in making more optimal decisions which can ultimately improve the overall performance. Apollo has been designed from the ground up to provide low latency, using Publish-Subscriber Pub-Sub semantics, and low overhead, using adaptive intervals in order to change the length of time between polling the resource for telemetry data and machine learning in order to predict changes to the telemetry data between actual resource polling. This work also provides some high level abstractions called I/O curators, which can further aid middleware libraries and applications to make optimal decisions. Evaluations showcase that Apollo can achieve sub-millisecond latency for acquiring complex insights with a memory overhead of ~57 MB and CPU overhead being only 7% more than existing state-of-the-art systems.