About News Publications
CV

Mohammad Hammoud

 

Address
Carnegie Mellon University
in Qatar
Education City
P.O.Box 24866
Doha, Qatar
Office

1006 Computer Science Department

Phone
+974 4454-8506
Email
mhhamoud@cmu.edu

I am an Assistant Teaching Professor at Carnegie Mellon University in Qatar (CMU-Q), wherein I teach and research distributed systems, cloud computing, database systems, and parallel computer architecture. I have a broad interest in anything that can help distributed and parallel computer systems perform faster and cooler. My current research focus is on designing and building scalable distributed systems for graph mining and database management systems for modern hardware architectures.


 

News

  • Our demo paper titled "DREAM in Action: A Distributed and Adaptive RDF System on the Cloud" was accepted at the 25th International World Wide Web Conference (WWW 2016)
  • We released the code of our DREAM system as open source. You can now download it from GitHub. Also, you can find more information about DREAM at the DREAM webpage
  • Our paper titled "Teaching Cybersecurity using the Cloud" was accepted at IEEE Transactions on Learning Technologies 2015
  • Our paper titled "DREAM: Distributed RDF Engine with Adaptive Query Planner and Minimal Communication" was accepted at VLDB 2015
  • Our NPRP Cycle 7 proposal titled "Scalable Analytics Engine for Big Graphs on the Cloud" was granted (award amount: $900K- Role: Lead PI)
  • I am always in the lookout for solid post-docs and research assistants/engineers. If you are interested to join our group, please send me an email with your CV.

Students and Research Engineers

  • Kenrick Fernandes (PhD student with Rami Melhem @ the University of Pittsburgh)
  • Karim Awara (PhD student with Panos Kalnis @ King Abdullah University of Science and Technology- KAUST)
  • Dania Abed Rabbou (Research Assistant)
  • Viswanath Krishnamurthy (Research Assistant)
  • Tamim Jabban (Research Assistant)

Teaching

At Carnegie Mellon University in Qatar (CMU-Q):

Prior to Joining CMU-Q:

  • CSI 412: Advanced Computer Architecture, Fall 2010 at the American University of Science and Technology (AUST), Lebanon
  • CSI 311: Java Programming, Fall 2010 at AUST
  • CSI 250: C++ Programming, Fall 2010 at AUST
  • CS110: Introduction to Personal Computers and the Internet, Fall 2007 at the University of Pittsburgh
  • CS 449: Introduction to Systems Software, Fall 2006 at the University of Pittsburgh (Lab Instructor)
  • CS/COE 1550: Introduction to Operating Systems, Summer 2006 at the University of Pittsburgh (Lab Instructor)

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Research Projects

GASA
Large-scale, graph-parallel computation is central to a wide array of applications ranging from data mining, through machine learning to natural language processing. In this project, we are designing a new highly scalable Graph-Aware Storage and Analytics (GASA) system, which can efficiently and effectively run on thousands of machines. On the storage layer side, we are devising a new graph-oriented distributed file system. On the analytics side, we are developing a distributed analytics engine with a best-of-breed graph-parallel abstraction at each cluster/cloud node. [Under Composition]
DREAM

The Resource Description Framework (RDF) and SPARQL query language are gaining widespread momentum and acceptance among various fields including science, bioinformatics, business intelligence and social networks, to mention a few. In this project, we are developing a novel Distributed RDF Engine with Adaptive query planner and Minimal communication (DREAM). The main goal of DREAM is to preclude data communication, while exploiting maximum parallelism. It seeks to achieve this goal via: (1) adopting a new paradigm of RDF systems, wherein all the advantages of the centralized and distributed RDF systems are combined and their disadvantages are avoided, and (2) exhibiting a polymorphic behavior, whereby its engine executes as either centralized or distributed (with various numbers of machines), depending on the complexities of the given SPARQL queries. [VLDB 2015]

Hadoop MapReduce
Hadoop MapReduce is a pervasive analytics engine for Big Data and is highly recognized for its elasticity, scalability and fault-tolerance. In this project, we are working on improving Hadoop network traffic and performance from two perspectives, systems and applications perspectives. On the systems side, we are modifying the Hadoop MapReduce code in order to incorporate new locality- and skew-aware scheduling algorithms. On the applications side, we are characterizing Hadoop performance (which does not require modifying Hadoop code) with various image processing, machine learning and text analytics applications so as to judiciously and synergistically guide Hadoop configuration on the cloud, and, accordingly, improve overall performance. [IEEE CloudCom 2011, IEEE CLOUD 2012, IEEE CLOUD 2013, Two Book Chapters on Virtualization and Analytics Engines for the Cloud with CRC Press]
Cloud Monitoring

Deploying performance-sensitive applications on the cloud is cumbersome due to the complexity of the cloud execution environment. Routine tasks such as monitoring, performance analysis and debugging often require close interaction and inspection of multiple layers within the cloud software infrastructure. In this project, we are designing and implementing user-oriented monitoring tools, which help integrate critical metrics into cloud layers and, accordingly, allow for flexible visualization and analysis. [IEEE CloudCom 2011]

CMP Cache Management

As large uniprocessors are no longer scaling in performance, Chip Multiprocessors (CMPs) have become the trend in computer architecture. CMPs can easily spread multiple threads of execution across various cores. Besides, CMPs scale across generations of silicon process simply by stamping down copies of the hard-to-design cores on successive chip generations. A key requirement to obtaining high performance from CMPs is to effectively manage the limited on-chip cache resources. In this project, we are: (1) designing a general framework for approaching cache management in CMPs, and (2) exploring novel CMP cache designs, which effectively employ our general framework and efficiently achieve scalable performance. [HiPEAC 2009, ICS 2009, CAL 2010, PhD Dissertation 2010, PACT 2010, HiPEAC 2011, JPDC 2011, A Book Chapter on Balancing Last Level Caches with CRC Press]

High-Performance Memory Substrates for Search-Intensive Applications

Search operations can occupy a significant portion of total execution time and energy consumption, while posing difficult performance problems upon using traditional memory hierarchy concepts. In this project, we are extending the conventional content addressable memory (CAM) to accelerate search operations present in various popular real-world applications (e.g., IP address lookup in core routers and trigram lookup in large speech recognition systems). [ISPASS 2007]

Power-Aware Memory Management using Software Generated Hints (In Collaboration with Intel)

Current state-of-the-art power-aware DRAM chips suggest various power modes (active, standby, nap, and power-down) offering, thereby, a way to reduce power consumption in the face of increasing demand for performance. In response to workloads becoming increasingly memory-intensive and data-centric, this project aims at exploiting the various power modes of DRAM chips for the most effective main memory power management. [Technical Report TR-09-163 (Proprietary of Intel)]


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Publications

  • Aisha Hasan, Mohammad Hammoud, Reza Nouri and Sherif Sakr, "DREAM in Action: A Distributed and Adaptive RDF System on the Cloud" Proceedings of the 25th International World Wide Web Conference (WWW 2016), Montreal, Canada, (To Appear).
  • Khaled Salah, Mohammad Hammoud and Sherali Zeadally, "Teaching Cybersecurity using the Cloud" IEEE Transactions on Learning Technologies, 2015 (To appear).
  • Mohammad Hammoud, Dania Abed Rabbou, Reza Nouri, Seyed-Mehdi-Reza Beheshti and Sherif Sakr, "DREAM: Distributed RDF Engine with Adaptive Query Planner and Minimal Communication" Proceedings of the 41st International Conference on Very Large Data Bases (VLDB), 2015 [PDF].
  • M. Suhail Rehman, Jason Boles, Mohammad Hammoud and Majd F. Sakr, "A Cloud Computing Course: From Systems To Services" Proceedings of the 46th ACM Special Interest Group on Computer Science Education Conference (SIGCSE), 2015 [PDF].
  • Mohammad Hammoud and Majd F. Sakr, "Distributed Programming for the Cloud: Models, Challenges and Analytics Engines" Large Scale and Big Data: Processing and Management, CRC Press, 2014 (A Book Chapter) [PDF].
  • Mohammad Hammoud and Majd F. Sakr, "Virtualizing Resources for the Cloud" Large Scale and Big Data: Processing and Management, CRC Press, 2014 (A Book Chapter) [PDF].
  • Mohammad Hammoud, Sangyeun Cho, and Rami Melhem, "FSB: Flexible Set Balancing Strategy for Last Level Caches" Multi-Core Technologies: Architectures, Algorithms, and Applications, CRC Press, 2013 (A Book Chapter) [PDF].
  • Mohammad Hammoud and Majd F. Sakr, "MC2: Map Concurrency Characterization for MapReduce on the Cloud" Proceedings of the 6th International Conference on Cloud Computing (CLOUD), Santa Clara, California, USA, June 2013 [PDF].
  • Mohammad Hammoud, M. Suhail Rehman and Majd F. Sakr, "Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic" Proceedings of the 5th International Conference on Cloud Computing (CLOUD), Honolulu, Hawaii, USA, June 2012 [PDF].
  • Mohammad Hammoud and Majd F. Sakr, "Locality-Aware Reduce Task Scheduling for MapReduce" Proceedings of the 3rd International Conference on Cloud Computing and Science (CloudCom), Athens, Greece, December 2011 [PDF].
  • M. Suhail Rehman, Mohammad Hammoud, Majd F. Sakr, "VOtus: A Flexible And Scalable Monitoring Framework for Virtualized Clusters" (Poster Paper), Proceedings of the 3rd International Conference on Cloud Computing and Science (CloudCom), Athens, Greece, December 2011 [PDF].
  • Mohammad Hammoud, Sangyeun Cho, and Rami Melhem, C-AMTE: A Location Mechanism for Flexible Cache Management in Chip MultiprocessorsJournal of parallel and Distributed Computing (JPDC), June 2011 [PDF].
  • Mohammad Hammoud, Sangyeun Cho, and Rami Melhem, Cache Equalizer: A Placement Mechanism for Chip Multiprocessor Distributed Shared CachesProceedings of the 6th Int'l Conference on High Performance and Embedded Architectures and Compilers (HiPEAC), Heraklion, Crete, Greece, January 2011 [PDF].
  • Mohammad Hammoud, Sangyeun Cho, and Rami Melhem, An Intra-Tile Cache Set Balancing Scheme. (Poster Paper), Proceedings of the Int'l Conference on Parallel Architectures and Compilation Techniques (PACT), Vienna, Austria, September 2010 [PDF].
  • Mohammad Hammoud, Hardware-Oriented Cache Management for Large-Scale Chip MultiprocessorsPhD Dissertation, August 2010 [PDF].
  • Mohammad Hammoud, Sangyeun Cho, and Rami Melhem, A Dynamic Pressure-Aware Associative Placement Strategy for Large Scale Chip MultiprocessorsJournal of IEEE Computer Architecture Letters (CAL), January-June 2010 [PDF].
  • Mohammad Hammoud, Sangyeun Cho, and Rami Melhem, Dynamic Cache Clustering for Chip MultiprocessorsProceedings of the ACM Int'l Conference on Supercomputing (ICS), IBM T. J. Watson Research Center, New York, June 2009 [PDF].
  • Mohammad Hammoud and Rami Melhem, Exploratory Efforts to Manage Power-Aware Memories using Software Generated Hints. Technical Report TR-09-163, Department of Computer Science, University of Pittsburgh (Proprietary of Intel).
  • Mohammad Hammoud, Sangyeun Cho, and Rami Melhem, ACM: An Efficient Approach for Managing Shared Caches in Chip Multiprocessors. In Proceedings of the 4th Int'l Conference on High Performance and Embedded Architectures and Compilers (HiPEAC) , Paphos, Cyprus, January 2009 [PDF].
  • Sangyeun Cho, Joel R. Martin, Ruibin Xu, Mohammad H. Hammoud and Rami Melhem. CA-RAM: A High-Performance Memory Substrate for Search-Intensive Applications. In IEEE Int'l Symposium on Performance Analysis of Systems and Software (ISPASS), San Jose, California, April 2007 [PDF].

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