14 citations · 29 across the 13 of their papers we have counts for
16 papers
TRANSIT: Transparent Scale-in for Multi-Node LLM Training
Hyungyo Kim, Nicholas Satchanov, Hrishi Shah +9
TRANSIT is a transparent scale-in framework to enable multi-node model training on fewer GPUs while maintaining training efficiency by transparently leveraging CPU DRAM as an exten…
Towards Continuous Profiling and Optimization of Quantum-Classical Pipelines
Ayush Bansal, Owen Cochell, Santiago Núñez-Corrales +4
Quantum applications increasingly execute as multi-stage quantum-classical pipelines, interleaving QPU computation with classical stages like circuit generation, transpilation, lay…
Examining QRMI as a Unified Interface for Quantum-HPC Integration
Thomas Badts, Tim Boyle, Claudio Carvalho +25
The efficient and scalable integration of quantum resources into high-performance computing (HPC) environments requires standardized mechanisms for resource management, scheduling,…
Quantum Computations on Fusion Blanket Molten Salts
Susanta Das, Thiago J. Pinheiro Dos Santos, Subhamoy Bhowmik +14
Molten salts such as FLiBe (2LiF--BeF) are leading blanket materials for breeding and recovering tritium in fusion reactors. Predicting tritium speciation requires accurate ele…
Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes
Kenneth M. Merz, Akhil Shajan, Danil Kaliakin +21
We develop a workflow decomposing a molecule into fragments via quantum embedding and simulating them with a heterogeneous quantum-classical (HQC) method. We sample fragment electr…
Reference Architecture of a Quantum-Centric Supercomputer
Seetharami Seelam, Jerry M. Chow, Antonio Córcoles +10
Quantum computers have demonstrated utility in simulating quantum systems beyond brute-force classical approaches. As the community builds on these demonstrations to explore using…
Observation of Improved Accuracy over Classical Sparse Ground-State Solvers using a Quantum Computer
William Kirby, Bibek Pokharel, Javier Robledo Moreno +25
Demonstrating quantum advantage over classical algorithms for ground state energy problems is an outstanding open problem in quantum computation. We experimentally demonstrate that…
Scaling Sample-Based Quantum Diagonalization on GPU-Accelerated Systems using OpenMP Offload
Robert Walkup, Juha Jäykkä, Igor Pasichnyk +14
Hybrid quantum-HPC algorithms advance research by delegating complex tasks to quantum processors and using HPC systems to orchestrate workflows and complementary computations. Samp…
Closed-loop calculations of electronic structure on a quantum processor and a classical supercomputer at full scale
Tomonori Shirakawa, Javier Robledo-Moreno, Toshinari Itoko +18
Quantum computers must operate in concert with classical computers to deliver on the promise of quantum advantage for practical problems. To achieve that, it is important to unders…
Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storage
Ziqi Yuan, Haoyang Zhang, Yirui Eric Zhou +4
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 frame…
Transforming the Hybrid Cloud for Emerging AI Workloads
Deming Chen, Alaa Youssef, Ruchi Pendse +42
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet th…
The infrastructure powering IBM's Gen AI model development
Talia Gershon, Seetharami Seelam, Brian Belgodere +143
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…
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Mayank Mishra, Matt Stallone, Gaoyuan Zhang +43
Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environmen…
Objcache: An Elastic Filesystem over External Persistent Storage for Container Clusters
Takeshi Yoshimura, Tatsuhiro Chiba, Sunyanan Choochotkaew +3
Container virtualization enables emerging AI workloads such as model serving, highly parallelized training, machine learning pipelines, and so on, to be easily scaled on demand on…
Hoard: A Distributed Data Caching System to Accelerate Deep Learning Training on the Cloud
Christian Pinto, Yiannis Gkoufas, Andrea Reale +2
Deep Learning system architects strive to design a balanced system where the computational accelerator -- FPGA, GPU, etc, is not starved for data. Feeding training data fast enough…
IBM Deep Learning Service
Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi +15
Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, te…