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Servesh Muralidharan - IEEE Xplore Author Profile
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Servesh Muralidharan

Publications
5
Citations
7
Publications by Year
20132024
Co-Authors:
Show All Co-Authors (45)
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Servesh Muralidharan

Affiliation

Argonne Leadership Computing Facility
Argonne National Laboratory
Lemont, IL, USA

Publication Topics

Biography

Servesh Muralidharan obtained his PhD in Computer Science from Trinity College Dublin. He pursued a postdoc at ICHEC and is currently a fellow at CERN. During his postdoc, he worked on the performance of parallel applications on accelerators such as the Xeon Phi, GPGPUs and FPGAs. His current research focus is on performance engineering of tools and techniques to optimize WLCG workloads.(Based on document published on 16 June 2017).
Publications
5
Citations
7
Publications by Year
20132024
Co-Authors:
Show All Co-Authors (45)
Author's Published Works

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Ponte Vecchio Across the Atlantic: Single-Node Benchmarking of Two Intel GPU Systems

Thomas Applencourt;Aditya Sadawarte;Servesh Muralidharan;Colleen Bertoni;JaeHyuk Kwack;Ye Luo;Esteban Rangel;John Tramm;Yasaman Ghadar;Arjen Tamerus;Chris Edsall;Tom Deakin

SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis
Year: 2024 | Conference Paper |
Intel Data Center GPU Max 1550, known as Ponte Vecchio (PVC), is a new Intel GPU architecture for high-performance computing. It is the basis of two systems on the June 2024 Top 500 list, Dawn (#51) and Aurora (#2).This work provides micro-benchmarking data on PVCs from which application developers may benefit, shows how the micro-benchmarking results are indicative of mini-app performance on PVC,...Show More

Ponte Vecchio Across the Atlantic: Single-Node Benchmarking of Two Intel GPU Systems

Thomas Applencourt;Aditya Sadawarte;Servesh Muralidharan;Colleen Bertoni;JaeHyuk Kwack;Ye Luo;Esteban Rangel;John Tramm;Yasaman Ghadar;Arjen Tamerus;Chris Edsall;Tom Deakin

Year: 2024 | Conference Paper |

MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization

Gautham Dharuman;Kyle Hippe;Alexander Brace;Sam Foreman;Väinö Hatanpää;Varuni K. Sastry;Huihuo Zheng;Logan Ward;Servesh Muralidharan;Archit Vasan;Bharat Kale;Carla M. Mann;Heng Ma;Yun-Hsuan Cheng;Yuliana Zamora;Shengchao Liu;Chaowei Xiao;Murali Emani;Tom Gibbs;Mahidhar Tatineni;Deepak Canchi;Jerome Mitchell;Koichi Yamada;Maria Garzaran;Michael E. Papka;Ian Foster;Rick Stevens;Anima Anandkumar;Venkatram Vishwanath;Arvind Ramanathan

SC24: International Conference for High Performance Computing, Networking, Storage and Analysis
Year: 2024 | Conference Paper |
We present a scalable, end-to-end workflow for protein design. By augmenting protein sequences with natural language descriptions of their biochemical properties, we train generative models that can be preferentially aligned with protein fitness landscapes. Through complex experimental-and simulation-based observations, we integrate these measures as preferred parameters for generating new protein...Show More

MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization

Gautham Dharuman;Kyle Hippe;Alexander Brace;Sam Foreman;Väinö Hatanpää;Varuni K. Sastry;Huihuo Zheng;Logan Ward;Servesh Muralidharan;Archit Vasan;Bharat Kale;Carla M. Mann;Heng Ma;Yun-Hsuan Cheng;Yuliana Zamora;Shengchao Liu;Chaowei Xiao;Murali Emani;Tom Gibbs;Mahidhar Tatineni;Deepak Canchi;Jerome Mitchell;Koichi Yamada;Maria Garzaran;Michael E. Papka;Ian Foster;Rick Stevens;Anima Anandkumar;Venkatram Vishwanath;Arvind Ramanathan

Year: 2024 | Conference Paper |

Efficient Multibyte Floating Point Data Formats Using Vectorization

Andrew Anderson;Servesh Muralidharan;David Gregg

IEEE Transactions on Computers
Year: 2017 | Volume: 66, Issue: 12 | Journal Article |
Cited by: Papers (6)
We propose a scheme for reduced-precision representation of floating point data on a continuum between IEEE-754 floating point types. Our scheme enables the use of lower precision formats for a reduction in storage space requirements and data transfer volume. We describe how our scheme can be accelerated using existing hardware vector units on two general-purpose processor (GPP) microarchitectures...Show More

Efficient Multibyte Floating Point Data Formats Using Vectorization

Andrew Anderson;Servesh Muralidharan;David Gregg

Year: 2017 | Volume: 66, Issue: 12 | Journal Article |

A Parallel Runtime Framework for Communication Intensive Stream Applications

Servesh Muralidharan;Kevin Casey;David Gregg

2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications
Year: 2013 | Conference Paper |
Stream applications are often limited in their performance by their underlying communication system. A typical implementation relies on the operating system to handle the majority of network operations. In such cases, the communication stack, which was not designed to handle tremendous amounts of data, acts as a bottleneck and restricts the performance of the application. In this paper, we propose...Show More

A Parallel Runtime Framework for Communication Intensive Stream Applications

Servesh Muralidharan;Kevin Casey;David Gregg

Year: 2013 | Conference Paper |

Heterogeneous Multiconstraint Application Partitioner (HMAP)

Servesh Muralidharan;Aravind Vasudevan;Avinash Malik;David Gregg

2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications
Year: 2013 | Conference Paper |
Cited by: Papers (1)
In this article we propose a novel framework -- Heterogeneous Multiconstraint Application Partitioner (HMAP) for exploiting parallelism on heterogeneous High performance computing (HPC) architectures. Given a heterogeneous HPC cluster with varying compute units, communication constraints and topology, HMAP framework can be utilized for partitioning applications exhibiting task and data parallelism...Show More

Heterogeneous Multiconstraint Application Partitioner (HMAP)

Servesh Muralidharan;Aravind Vasudevan;Avinash Malik;David Gregg

Year: 2013 | Conference Paper |

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