Welcome to my homepage! I (Eric, or Zhiqiang) am a performance and latency sensitive computer systems designer and builder, with experiences of designing/building or leading teams to build scalable systems like high performance parallel data processing platforms for big data, large scale (PBs) data storage systems, blockchain system for high throughput and low latency transaction processing.

I received his PhD degree from the Department of Computer Science and Engineering of The Hong Kong University of Science and Technology in 2014 and the bachelor’s degree in Computer Science from Fudan University in 2009.

You can reach me by Email at zma [at] ericzma.com .

Projects and products I ever worked on

Some of the systems and projects I ever worked on are as follows.

Industrial projects and products

  • ParallelChain: a high performance low latency permissioned and permissionless hybrid private blockchain system, horizontally scalable to 100K synchronous transactions per second per instance with 0.1 second transaction on-chain confirmation latency. [Product page]
  • PreventiveChain: preventive security access control system with all records secured by ParallelChain. [Product page]
  • ChattelChain: digital security (token) platform on ParallelChain, supporting asset tokenization and digital security/token exchange trading. [Product page]
  • ConstructionChain: a web app + mobile app solution for work inspection workflow using ParallelChain to facilitate and track immutable activities records, streaming the workflow and data management and improving accuracy and efficiency. [Product page]
  • Data Thinker (D-thinker): A general, efficient and easy-to-use distributed big data processing engine that is several times faster Spark and 10s of times faster than Hadoop, supporting Java, Python, C/C++, puc, c0/Layer Zero and etc. [Product page] [Homepage] [Poster (in Chinese)]
  • D-store: A scalable, fault-tolerant and easy-to-use distributed large scale data storage system with various underlying storage modules like HDFS and Ceph, supporting data synchronization over WAN.
  • GLAD: A high-speed parallel genome analysis system making genome analysis easy and fast on D-thinker. Accelerating analysis by 10s of times on server clusters without requiring much changes to original sequential pipeline implementations. [Product page] [Whitepaper of one workflow on GLAD]
  • ThinkBox/Genes’ Mind Big Data Appliance: a high-performance big data storage processing hardware and software integrated appliance system. [Product page]

Research projects

  • Layer Zero (L0): A general-purpose substrate for cloud computing. [Zhiqiang’s PhD Thesis: download here or from HKUST Library]
  • VOLUME (Virtual On-Line Unified Memory Environment): A distributed virtual memory that unifies the physical memory and disk resources on many compute nodes and form a system-wide data substrate to support large-scale in-memory computation. [CloudCom’13 paper]
  • DVM: A distributed virtual machine that comprises a large number of physical hosts. [TC paper] [VEE’12 paper]
  • MRlite: A lightweight and efficient parallelization framework for MapReduce-style computation. [CLOUD COMPUTING 2010 paper] [mrcc]
  • CCMR: A cloud computing testbed for systems research and scientific computing at HKUST. [Homepage (archive)] [a short description in Section 4 of DVM paper]



Patents and tech reports

Presentations, demos and professional activities

  • Panel Speaker. In "Big Data Startups" forum for Hong Kong Big Data Symposium 2015. May. 28, 2015. Hong Kong Science Park.
  • External reviewer. For IEEE INFOCOM 2015.
  • Large-Scale In-Memory Data Processing. PhD Thesis Defence. Aug. 12, 2014, HKUST. [Abstract] [PDF from HKUST library]
  • Large-Scale In-Memory Data Processing. PhD Thesis Proposal Defence. Apr. 9, 2014, HKUST. [Abstract]
  • Layer Zero: An Efficient Platform for Cloud Computing. Poster and demo (Performance comparison between Layer Zero and Hadoop for k-means clustering) at China Hi-Tech Fair 2012 (CHTF 2012). Nov. 16-21, 2012, Shenzhen, China. [Poster, 8.0MB, in Chinese]
  • Layer Zero: Building Software Infrastructure for Cloud Computing. Invited talk and demo at the Institute of Software, Chinese Academy of Sciences (ISCAS). Aug. 16, 2012, Beijing, China.
  • DVM: Towards a Datacenter-Scale Virtual Machine. The 2012 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE 2012). Mar. 3-4, 2012, London, UK. [Slides, 2.3MB, best viewed in presentation mode]
  • Survey on Programming and Tasking in Cloud Computing Environments. PQE at HKUST. Feb. 18, 2011, HKUST. [Abstract] [Slides, 2.5MB]
  • The Limitation of MapReduce: A Probing Case and a Lightweight Solution. The 1st Intl. Conf. on Cloud Computing, GRIDs, and Virtualization (CLOUD COMPUTING 2010). Nov. 21-26, 2010, Lisbon, Portugal. [Slides, 1.5MB]


I ever worked as teaching assistants for the following courses at HKUST.

  • COMP 4611: Design and Analysis of Computer Architectures (Fall 2012)
  • COMP 4622: Computer Communication Networks II (Fall 2011)
  • COMP 362: Computer Communication Networks II (Fall 2010)
  • COMP 231: Database Systems (Spring 2010)
  • COMP 102: Computer and Programming Fundamental I (Fall 2009)

Software releases and tutorials contributed

  • cc0 – a compiler for the C0/Puc language. [Puc manual]
    • Puc is a C-like language with linguistic support for massive parallelism on a modern compute cluster.
  • libi0 – libi0 is a general library for writing Puc code targeted i0. [Code tree]
    • i0 the ISA for Data Thinker which unifies myriads of nodes in the datacenters to form a big virtual machine.
  • mrcc – A Distributed C Compiler System on MapReduce. [Homepage]
    • An efficient distributed compilation tool for cloud computing systems based on Hadoop or MRlite.
  • zlog – A C In-memory Logging Library. [Homepage]
    • A low-latency, efficient and easy-to-use in-memory logging library.
  • SysTutorials – Tutorials, howtos and tips on Linux and more. [Eric’s posts]

Latest posts

Contact and social networks

You can leave Eric an message by using this form or find Eric on Facebook, Twitter and LinkedIn.

More of Eric’s contact information: https://www.ericzma.com/blog/contact/


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