Mingliang LIU

No news is good news. now
Elected as Apache Hadoop Committer. See my Hadoop blog. Aug 2016
APPRIME was accepted by SIGMETRICS as a regular paper (32/239). Feb 2015
ACIC (extended) was published by IEEE TPDS journal. Dec 2014
Graduated from Tsinghua University with PhD degree. Jul 2014
ACIC is accepted by SC 2013 as a technical paper (92/457). Jul 2013

IIO: Automatic I/O Benchmark Generation by Slicing Parallel Applications

The idea of the IIO is to obtain a program slice through static analysis, and to generate a compilable and human-readable benchmark from it. We generate the benchmark from original parallel application by reducing those irrelevant statements, while preserving all the variables and statements in the original program relevant to the spatial and volume attributes.

APPRIME: Automated Trace Profiler and Benchmark Generator for Parallel Applications

It is very challenging and costly to obtain high-fidelity benchmarks reflecting the scale and complexity of state-of-the-art parallel applications. Taking as input standard communication-I/O traces of an application’s execution, APPRIME couples accurate automatic phase identification with statistical regeneration of event parameters to create compact, portable, and to some degree reconfigurable parallel application benchmarks, which retain the original applications’ performance characteristics, in particular the relative performance across platforms.

Giri: Dynamic Program Slicing in LLVM

Dynamic program slicing is a technique that can precisely determine which instructions affected a particular value in a single execution of a program. This project was firstly developed by Sahoo, Swarup Kumar and John Criswell under Vikram S. Adve from UIUC. It was selected by the Google Summer of Code (GSoC) 2013, under its umbrella project LLVM.

ACIC: Automatic Cloud I/O Configurator for Parallel Applications

ACIC takes a given application running on a given cloud platform, and automatically searches for optimized I/O system configurations. ACIC utilizes machine learning models to perform black-box performance/cost predictions. To tackle the high-dimensional parameter exploration space unique to cloud platforms, we enable affordable, reusable, and incremental training guided by Plackett and Burman Matrices. Results with four representative applications indicate that ACIC consistently identifies near-optimal configurations among a large group of candidate settings.

Tsinghua University

PhD in Computer Science and Technology Aug 2007 - Jul 2014

Three Gorges University

B.S. in Information Systems Sep 2002 - Jul 2006

Scholarship for overall excellence - 1st Class, Tsinghua University Oct 2013
Scholarship for overall excellence - 3rd Class, Tsinghua University Oct 2012
Scholarship for overall excellence - 1st Class, Tsinghua University Oct 2011
Scholarship for overall excellence - 2nd Class, Tsinghua University Oct 2008
Outstanding student cadres - Tsinghua University May 2008
National scholarship - 2nd class, Three Gorges University Sep 2004

• Ye Jin, Xiaosong Ma, Mingliang Liu, Qing Liu, Jeremy Logan, Norbert Podhorszki, Jong Youl Choi, and Scott Klasky. Combining Phase Identification and Statistic Modeling for Automated Parallel Benchmark Generation. To Appear in ACM SIGMETRICS conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2015. (Paper | BibTex)
• Jidong Zhai, Mingliang Liu, Ye Jin, Xiaosong Ma and Wenguang Chen. Automatic Cloud I/O Configurator for I/O Intensive Parallel Applications. IEEE Transactions on Parallel and Distributed Systems (TPDS), 2014. (Paper | BibTex)
• Mingliang Liu, Ye Jin, Jidong Zhai, Yan Zhai, Qianqian Shi, Xiaosong Ma and Wenguang Chen. ACIC: Automatic Cloud I/O Configurator for HPC Applications. In Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis (SC). ACM, 2013. (Paper | Slides | BibTex)
• Mingliang Liu, Ye Jin, Jidong Zhai, Yan Zhai, Qianqian Shi, Xiaosong Ma, and Wenguang Chen. Automatic Cloud I/O Configurator for Parallel Applications. In International ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC) Poster Session. 2013. (Poster | BibTex)
• Mingliang Liu, Jidong Zhai, Yan Zhai, Xiaosong Ma, and Wenguang Chen. One Optimized I/O Configuration per HPC Application: Leveraging the Configurability of Cloud. In Proceedings of the second ACM Asia-Pacific Workshop on Systems (APSys). ACM, 2011. (Paper | Slides | BibTex)
• Yan Zhai, Mingliang Liu, Jidong Zhai, Xiaosong Ma, and Wenguang Chen. Cloud Versus In-house Cluster: Evaluating Amazon Cluster Compute Instances for Running MPI Applications. In Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis (SC). ACM, 2011. (Paper | Slides | BibTex)
• Fucen Zeng, Lin Qiao, Mingliang Liu, and Zhizhong Tang. A Novel Memory Subsystem Evaluation Framework for Chip Multiprocessors. In International Conference on High Performance and Communications (HPCC). IEEE, 2010. (Paper | BibTex)
• Since this is the last line, I don't have enough space for more publications.