arXiv subjects

Apoorve Mohan

Publications and source records attributed to Apoorve Mohan.

7 recordsLinked to original sources

arXiv2026

Towards Continuous Profiling and Optimization of Quantum-Classical Pipelines

Quantum applications increasingly execute as multi-stage quantum-classical pipelines, interleaving QPU computation with classical stages like circuit generation, transpilation, layout mapping, quantum error mitigation (QEM), and post-processing. These stages have diverse resource requirements and exhibit stochastic behavior under drifting hardware noises, yet existing workflow frameworks treat them as static, isolated components. We present LLQM (Low-Level Quantum Machine), a profiling-driven meta-framework for quantum-classical pipelines. LLQM decomposes pipelines into fine-grained tasks and continuously profiles their CPU/GPU, memory, QPU, and queue dependencies alongside real-time hardware states. This unified runtime abstraction captures cross-stage resource dependencies and reveals how classical and quantum decisions interact, enabling characterization of their impact on fidelity and resource consumption. We evaluate LLQM using QEM as a representative pipeline stage, on IBM 156-qubit Heron r2 processors with circuits up to 100 qubits and 1e7 transpiled gates. Our results show that continuous profiling exposes runtime bottlenecks and enables hardware-, fidelity-, and workload-aware optimizations.

quant-ph↗

arXiv2026

Tuning Collective Patterns to Alleviate Congestion in Shared AI Clusters

Distributed AI training involves recurring rounds of data exchange between multiple pairs of GPU nodes. Slowdown in even one flow due to congestion can cause the entire communication round to slowdown. Current approaches for evading congestion in AI clusters assume global control over the entire workload (e.g. coordinating the schedule of all jobs) or assume infrastructural support (e.g. adaptive routing in switches). They are thus ill-suited in a shared cloud setting where AI jobs belonging to one user can face external congestion from other users' jobs or background traffic beyond its own control. In this paper, we build a system, REACT, that tunes the recurring pattern of data exchange between GPU nodes (known as communication collectives) in response to congestion. REACT works at the application (communication library) layer, where it detects congestion at runtime using readily available flow stats, and tunes the collective pattern to alleviate congestion - changing the set of incident flows while retaining the semantics of information exchange (e.g. selecting which node aggregates data in an AllReduce tree). REACT requires no explicit support from the underlying network infrastructure and can be unilaterally deployed by individual users in a shared cloud setting. We prototype REACT as a shim layer over NCCL, and evaluate it on a shared academic GPU cluster - enabling REACT improves communication performance (algorithm bandwidth) by 13%-38% under network congestion. Our simulations across a range of congestion scenarios further reveal up to 75% performance improvement, highlighting the effectiveness of our approach.

cs.NI↗

arXiv2025

Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storage

We present the design and implementation of a new lifetime-aware tensor offloading framework for GPU memory expansion using low-cost PCIe-based solid-state drives (SSDs). Our framework, TERAIO, is developed explicitly for large language model (LLM) training with multiple GPUs and multiple SSDs. Its design is driven by our observation that the active tensors take only a small fraction (1.7% on average) of allocated GPU memory in each LLM training iteration, the inactive tensors are usually large and will not be used for a long period of time, creating ample opportunities for offloading/prefetching tensors to/from slow SSDs without stalling the GPU training process. TERAIO accurately estimates the lifetime (active period of time in GPU memory) of each tensor with the profiling of the first few iterations in the training process. With the tensor lifetime analysis, TERAIO will generate an optimized tensor offloading/prefetching plan and integrate it into the compiled LLM program via PyTorch. TERAIO has a runtime tensor migration engine to execute the offloading/prefetching plan via GPUDirect storage, which allows direct tensor migration between GPUs and SSDs for alleviating the CPU bottleneck and maximizing the SSD bandwidth utilization. In comparison with state-of-the-art studies such as ZeRO-Offload and ZeRO-Infinity, we show that TERAIO improves the training performance of various LLMs by 1.47x on average, and achieves 80.7% of the ideal performance assuming unlimited GPU memory.

cs.DC↗

arXiv2025

The infrastructure powering IBM's Gen AI model development

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings.

cs.DC↗

arXiv2019

Supporting Security Sensitive Tenants in a Bare-Metal Cloud

Bolted is a new architecture for bare-metal clouds that enables tenants to control tradeoffs between security, price, and performance. Security-sensitive tenants can minimize their trust in the public cloud provider and achieve similar levels of security and control that they can obtain in their own private data centers. At the same time, Bolted neither imposes overhead on tenants that are security insensitive nor compromises the flexibility or operational efficiency of the provider. Our prototype exploits a novel provisioning system and specialized firmware to enable elasticity similar to virtualized clouds. Experimentally we quantify the cost of different levels of security for a variety of workloads and demonstrate the value of giving control to the tenant.

cs.DC↗

arXiv2019

A Secure Cloud with Minimal Provider Trust

Bolted is a new architecture for a bare metal cloud with the goal of providing security-sensitive customers of a cloud the same level of security and control that they can obtain in their own private data centers. It allows tenants to elastically allocate secure resources within a cloud while being protected from other previous, current, and future tenants of the cloud. The provisioning of a new server to a tenant isolates a bare metal server, only allowing it to communicate with other tenant's servers once its critical firmware and software have been attested to the tenant. Tenants, rather than the provider, control the tradeoffs between security, price, and performance. A prototype demonstrates scalable end-to-end security with small overhead compared to a less secure alternative.

cs.DC↗

arXiv2018

M2: Malleable Metal as a Service

Existing bare-metal cloud services that provide users with physical nodes have a number of serious disadvantage over their virtual alternatives, including slow provisioning times, difficulty for users to release nodes and then reuse them to handle changes in demand, and poor tolerance to failures. We introduce M2, a bare-metal cloud service that uses network-mounted boot drives to overcome these disadvantages. We describe the architecture and implementation of M2 and compare its agility, scalability, and performance to existing systems. We show that M2 can reduce provisioning time by over 50% while offering richer functionality, and comparable run-time performance with respect to tools that provision images into local disks. M2 is open source and available at https://github.com/CCI-MOC/ims.

cs.DC↗