Often, in a large parallel program, the pattern of communication changes dramatically as the program runs. For this reason, there may be no single programming paradigm that suits the entire application well. The Charm++ runtime system provides a way for the programmer to seamlessly use the most appropriate paradigm for each phase of the program. I present ParFUM, a framework for unstructured meshes, as an example of this technique which takes advantages of three ways of writing parallel programs: MPI, Charm++, and MSA.

In this talk we discuss our research effort at PPL that provide the programmer with a global view of data (Multiphase Shared Arrays) and a global view of control (Charisma) to complement Charm++'s local view of data and local view of control. These higher-level languages are based on the same adaptive run-time system and aimed to provide higher productivity for parallel programming.

NAMD is a widely used program for molecular dynamics simulations of large biomolecular systems. Running on everything from laptops to supercomputers, NAMD must provide robustly scalable parallelism in the hands of ordinary biomedical researchers. The talk will discuss how NAMD is currently being adapted to run on both upcoming petascale machines and commodity clusters with NVIDIA CUDA graphics processors.

PetaFLOPS-class computers are currently being developed and even larger computers are being planned. Our BigSim project is aimed at developing tools that allow one to develop, debug and tune/scale/predict the performance of applications before such machines are available so that the applications can be ready when the machine first becomes operational. It also allows easier "offline" experimentation of parallel performance tuning strategies --- without using the full parallel computer. To the machine architects, BigSim provides a method for modeling the impact of architectural choices (including the communication network) on actual, full-scale applications. In this talk, we will present our simulation framework which consists of an emulator and a simulator; we will focus on the recent progress in integrating instruction level simulation with our framework.

The Cell processor, jointly developed by IBM, Sony, and Toshiba, has great computational power when compared to other commodity processors. For this reason, we would like to adapt the Charm++ runtime system to be able to harness the power of the Cell. While the design of the Cell processor is the source of this computational power, it is also the source of several difficulties such as ease of programming and portability. In this talk, we will be discussing our efforts to port the Charm++ runtime system, and thus the Charm++ programming model, to Cell-based platforms. There are several aspects of the Charm++ programming model that make it a good fit for the Cell processor, including: data encapsulation, virtualization, message queue peek-ahead, and so on. We also believe that the Charm++ can help with several of the difficulties facing the Cell.

For more information, please see:

Recent Cell Paper

Cell Research Page

Recent Cell Poster (WARNING: 33MB PDF File)

The scalability of performance tools in high performance computing has been lagging behind the growth in the sizes of supercomputers and the applications that run on them. The volume of performance trace data generated easily becomes unmanagable without appropriate controls as we scale upwards. At the same time, the amount of information that has to be presented to a human analyst can also become overwhelming.

We present techniques used to address the above problems and enhance the scalability of Projections, a performance instrumentation and visualization framework for the migratable object programming model Charm++. Projections provides multiple resolutions of performance data. We couple this feature with the use of heuristics and clustering algorithms at application runtime to provide powerful mechanisms to reduce performance data volume and analysis time while preserving data relevance. We employ similar heuristics and algorithms for enhanced interactive post-mortem visualization and analysis assistance to a human expert.

Important scientific problems can be treated via ab initio based molecular modeling approaches wherein atomic forces are derived from an energy function that explicitly considers the electrons. The Car-Parrinello ab initio molecular dynamics method (CPAIMD) is widely used to study systems containing hundreds to thousands of atoms. However, CPAIMD's impact has been limited due to difficulties inherent in scaling the technique beyond processor numbers about equal to the number of electronic states, until recent efforts by ourselves and others. CPAIMD computations involve a large number of interdependent phases with high communication overhead including multiple concurrent sparse 3D-Fast-Fourier Transforms (3D-FFTs), non-square matrix multiplies and few concurrent dense 3D-FFTs.

Using Charm++ and its concept of processor virtualization, the phases are discretized into a large number of virtual processors which are, in turn, mapped flexibly onto physical processors, thereby allowing significant interleaving of work. Interleaving is enhanced through both architecturally independent methods and network topology aware mapping techniques. Algorithmic and Blue Gene/L specific optimizations are employed to scale CPAIMD to 20480 nodes, about 30-times the number of electronic states in the largest benchmark system presented.

The enormous dynamic range involved in forming galaxies in their cosmological context continues to tax the capabilities of the largest computers available. Galaxies are influenced by the gravitational forces originating tens of megaparsecs away, while the star formation process which ultimately leads to galaxies being visible occurs on sub-parsec scales. The large range in length scales also imply a large range in timescales, hence spatially and temporally adaptive algorithms are needed for efficiently performing these calculations. Strategies for using the cabilities of the Charm++ system for tackling these challenges will be discussed.

In order to scale cosmological simulations to PetaScale computers, we built ChaNGa: a cosmological simulator which has recently been released to the public. This simulator has shown outstanding scalability to machines with tens of thousands of processors.

In this talk I will describe the main features of this code based on the Charm++ Runtime System, focusing in particular on issues regarding load balancing and multistepping. I will accompany my description with results from large machines, such as IBM-BlueGene/L, Cray-XT3 and commodity clusters.