collaborators

9 papers

cs.AR2026

GRAINS: Storage-Aware Algorithm-Architecture Co-Design Enabling High-Performance and Low-Cost Graph-Based Genome Analysis

Nika Mansouri Ghiasi, Harun Mustafa, Talu Güloglu +8

Graph-based representations of genome sequences have emerged as a powerful approach for representing massive genomic databases in an expressive and efficient way. Despite their ben…

cs.AR2026

Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learning

Rakesh Nadig, Vamanan Arulchelvan, Rahul Bera +7

Modern high-performance computing (HPC) environments rely on hybrid storage systems (HSS) that combine multiple storage devices with diverse latency, bandwidth, endurance, and capa…

cs.AR2026

Machine Learning-Driven Intelligent Memory System Design: From On-Chip Caches to Storage

Rahul Bera, Rakesh Nadig, Onur Mutlu

Despite the data-rich environment in which memory systems of modern computing platforms operate, many state-of-the-art architectural policies employed in the memory system rely on…

cs.AR2026

Conduit: Programmer-Transparent Near-Data Processing Using Multiple Compute-Capable Resources in Solid State Drives

Rakesh Nadig, Vamanan Arulchelvan, Mayank Kabra +9

Solid-state drives (SSDs) are well suited for near-data processing (NDP) because they: (1) store large application datasets, and (2) support three NDP paradigms: in-storage process…

cs.AR2026

SAGe: A Lightweight Algorithm-Architecture Co-Design for Mitigating the Data Preparation Bottleneck in Large-Scale Genome Sequence Analysis

Nika Mansouri Ghiasi, Talu Güloglu, Harun Mustafa +8

Genome sequence analysis, which examines the DNA sequences of organisms, drives advances in many critical medical and biotechnological fields. Given its importance and the exponent…

cs.CL2025

REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing

Kangqi Chen, Andreas Kosmas Kakolyris, Rakesh Nadig +7

Large Language Models (LLMs) face an inherent challenge: their knowledge is confined to the data that they have been trained on. To overcome this issue, Retrieval-Augmented Generat…