Mark P. Blanco

I am a PhD Candidate at the Carnegie Mellon University Department of ECE.

My PhD work focuses on high-performance algorithms, architectures, and techniques for graph processing and development of analytical performance models for scientific (HPC) workloads.

I am interested in research or work in industry where I will have an impact on computer architectures, performance, and workloads that drive science and society.

Research Interests

Graph processing, performance modeling, High Performance Computing (HPC), computer architecture, scientific computing

Education

Expected Graduation: December 2022 PhD Candidate

Carnegie Mellon University, Pittsburgh, PA

Graduated: May 2019 Master of Science

Carnegie Mellon University, Pittsburgh, PA

Graduated: May 2017 Bachelor of Science

Rensselaer Polytechnic Institute, Troy, NY

Publications

[Outstanding Student Paper] M. P. Blanco, S. McMillan, T. M. Low, “Delayed Asynchronous Iterative Graph Algorithms,” presented at the 2021 IEEE High Performance Extreme Computing Conference (HPEC), held virtually.

Azad et al. “Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite,” 2020 IEEE International Symposium on Workload Characterization

M. P. Blanco, S. McMillan, T. M. Low, “Towards an Objective Metric for the Performance of Exact Triangle Count,” presented at the 2020 IEEE High Performance Extreme Computing Conference (HPEC), held virtually.

[Graph Challenge Champion] M. Blanco, T. M. Low, and K. Kim, “Exploration of Fine-Grained Parallelism for Load Balancing Eager K-truss on GPU and CPU,” presented at the 2019 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA, p. 7.

U. Sridhar, M. Blanco, R. Mayuranath, D. G. Spampinato, T. M. Low, and S. McMillan, “Delta-Stepping SSSP: From Vertices and Edges to GraphBLAS Implementations,” in 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2019, pp. 241–250.

Carothers et al. 2017. “Durango: Scalable Synthetic Workload Generation for Extreme-Scale Application Performance Modeling and Simulation,” In Proceedings of the 2017 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS 2017).

Mandal et al. 2016. “Toward an end-to-end framework for modeling, monitoring and anomaly detection for scientific workflows.” Parallel and Distributed Processing Symposium Workshops, 2016 IEEE International.

Awards

Fall 2019 to Present NSF Graduate Fellowship for Computer Engineering (On tenure)

September 2021 Outstanding Student Paper award at IEEE High Performance Computing 2021

September 2019 Graph Challenge Champion at High Performance Extreme Computing

Work Experience

Summer 2021 Graduate Research Intern

CMU Software Engineering Institute, AI Division, Pittsburgh PA

Summer 2019 Graduate Research Intern

Sandia National Laboratories, Albuquerque NM

Spring and Fall 2018 Graduate Teaching Assistant

Carnegie Mellon University, Pittsburgh PA

Summer 2016 & 2017 Software Development Engineering Intern

Microsoft Corporation, Redmond WA

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Summer & Fall 2015 Undergraduate Computer Science Researcher

Rensselaer Polytechnic Institute Troy, NY

Spring 2014 - Fall 2014 Advising and Learning Assistance Center Tutor

Rensselaer Polytechnic Institute Troy, NY

Course Projects

Fall 2018 Social Circle Analysis Project in Machine Learning (10-701)

Spring 2018 RL DVFS Governors Work done in System Level Design group

Spring 2018 Matrix Inversion Accelerator Project in Computer Architecture (18-742)

Spring 2018 Parallel and Distributed SGD Project in Optimization (18-660)

Fall 2017 Multi-kernel CNN Accelerator Project in Reconfigurable Logic (18-643)

Spring 2017 Parallel Finite Element Analysis Project in Parallel Programming for Engineers