Research Interests

The main focus of my research is optimizations for distributed memory programs (MPI). We employ static analysis, program transformations combined with dynamic optimizations to improve performance (or power efficiency) of parallel programs. Machine learning techniques are used to make these optimizations feasible and effective for the target architectures.

  • Parallel Programming Models
  • Optimizations & Program transformations
  • Compilers
  • Multi-/many-core Architectures
  • Message-passing
  • Machine Learning Techniques

Education

  • Master Degree in Software Engineering, University of Bologna, 2006
    • Topic - "Generic types support in the Java Virtual Machine: Design and prototype implementation"

News


Access to the Intel Single-chip Cloud Computer (August 2010)

The University of Innsbruck gain access to the Intel Single-chip Cloud Computer for a three-years research project. The Intle SCC is a research microprocessor containing the most Intel Architecture cores ever integrated on a silicon CPU chip - 48 cores. It incorporates an on-chip-network intended to scale multi-core processors to 100 cores and beyond, advanced power management technologies and support for message-passing.

The goal of our project will be the realization of an MPI layer on top of RCCE, i.e. a message passing library specifically designed for the Intel SCC. Based on HPC-like workload we will then define compiler optimizations which improve the scalability of MPI programs on the chip.
EuroPVM/MPI 2009 Report (7-10 Sept. 2009, Espoo)

EuroPVM/MPI is the main international conference which exclusively targets MPI programming. Although the number of participants was slightly decreased this years, the quality of the presented papers was above average. Beside the ideas and insights received during the 4 days conference, it has been a great pleasure to meet and talk with some of the most relevant figures in the parallel computing community (e.g. Jack Dongarra, William Gropp, etc...).

Several talks pointed out the leadership of the MPI programming model in High Performance Computing (HPC) which, as now, is able to efficiently scale up to a million processors. Furthermore, MPI is looking forward petascale and future exascale systems. As the number of cores per node is increasing, the MPI community is also addressing the problem of interoperability with other models (shared memory) and thus making hybrid programming (e.g. MPI+OpenMP, MPI+PGAS) simpler.

However, in my opinion, MPI must catch up with the current state of parallel programming. The C/Fortran interface is too old, somehow difficult and error prone. Today, other models (like the actor model) are gaining more and more interest (take a look at Scala and Erlang) as they promise distributed memory programming in an easy flavour. However, these languages provides only a subset of the functionalities already present in MPI and their implementations are not able to take full advantages of the underlying hardware (e.g. InfniBand) as MPI does.

Unfortunately there are no efforts in simplifying the usage of MPI (from the user point of view) and the impression is that MPI is destined to remain a "niche" interface for HPC world where thousand or more nodes are involved. In my opinion this is a pity as message-passing implicitly encourage data locality in its model and in a future where the number of cores per chip is rapidly increasing and the cache coherency systems are breaking down, a flavour of distributed memory programming on a single chip will be probably faced again!

Test

It works! ...and it's cool!