Mark P. Blanco

PhD Candidate in Electrical Engineering at Carnegie Mellon University

Research interests

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

Education

Started: Sept 2017 Carnegie Mellon University, Pittsburgh, PA

Pursing PhD in High Performance Modeling and Computation
Advisor: Dr. Tze Meng Low, ECE Department

Graduated: May 2017 Rensselaer Polytechnic Institute, Troy, NY

Dual Major in Computer Systems Engineering and Computer Science
Member of Upsilon Pi Epsilon and Eta Kappa Nu Honor Societies

Publications

(Accepted and pending publication): 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.
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

2019 NSF Graduate Fellowship for Computer Engineering (On tenure)

2019 Graph Challenge Champion at High Performance Extreme Computing

Work Experience

Summer 2019 Graduate Research Intern

Sandia National Laboratories, Albuquerque NM

Collaborated with researchers at Sandia National Labs to model and optimize a molecular dynamics approach called SNAP.
Analyzed existing code base performance on CPU architectures from ARM, IBM, and Intel.
Rewrote existing approach to take advantage of SIMD hardware on all three CPU architectures and worked to develop an analytical performance model for the new approach.
Attained 1.66x to 3.22x end-to-end application speedups using new approach and auto-vectorization in GCC.

Spring and Fall 2018 Graduate Teaching Assistant

Carnegie Mellon University, Pittsburgh PA

Mentored students in lectures and in office hours on design of efficient, high performance code
Taught benchmarking techniques utilizing x86 ASM, compiler optimizations, use of SIMD,principles of memory hierarchy, and parallelism via OpenMP and MPI

Summer 2016 & 2017 Software Development Engineering Intern

Microsoft Corporation, Redmond WA

'17

Applied data science techniques to improve Office Admin Portal based on customer feedback '16
Iterated on discussions with team members to develop and refine customer feedback analysis

Summer & Fall 2015 Undergraduate Computer Science Researcher

Rensselaer Polytechnic Institute Troy, NY

Created MPI-based hybrid simulator for parallel workload modeling on supercomputers
Learned principles of efficient distributed code for use on HPC systems
Work incorporated into publication of Durango in SIGSIM-PADS’17

Spring 2014 - Fall 2014 Advising and Learning Assistance Center Tutor

Rensselaer Polytechnic Institute Troy, NY

Tutored peers in computer science (Python and C++) in group and one-on-one settings

Projects

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

Applied autoencoders & unsupervised clustering on social network structure and content

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

Prepared DVFS framework for students in 18-651
Presented poster on thermal-aware Reinforcement Learning at MLSE’18

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

Explored design space of cache and scratchpad accelerators using Gem5-Aladdin framework

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

Implemented and tested scalability of parallel and distributed SGD algorithms

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

Designed and demonstrated layer-optimized CNN kernels on Zynq FPGA

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

Implemented 3D method of stiffness for truss deformation in OpenMP & CuBLAS

C/C++, C#, MPI, CUDA, OpenMP
MATLAB, Bash, JavaScript, Python, Git
Experience in technical writing and presentation
Vivado HLS, embedded programming, PCB design
Linux kernel programming
Conversant in French and Spanish