Chapter-3 Software and System Management OF SUPERCOMPUTER 3.1 Software and System Management of Supercomputer: 9  3.1.1 Supercomputer Operating Systems: Since the end of the 20th century, supercomputer operating systems have undergone major transformations, as fundamental changes have taken place in supercomputer architecture. While early operating systems were custom tailored to each supercomputer to gain speed, the trend has been to move away from in-house operating systems to the adaptation of generic software such as Linux. Figure A.4: The supercomputer center at NASA Ames. Given that modern massively parallel supercomputers typically separate computations from other services by using multiple types of nodes, they usually run different operating systems on different nodes, e.g. using a small and efficient lightweight kernel such as CNK or CNL on compute nodes, but a larger system such as a Linux-derivative on server and I/O nodes. While in a traditional multi-user computer system job scheduling is in effect a tasking problem for processing and peripheral resources, in a massively parallel system, the job management system needs to manage the allocation of both computational and communication resources, as well as gracefully dealing with inevitable hardware failures when tens of thousands of processors are present. Although most modern supercomputers use the Linux operating system, each manufacturer has made its own specific changes to the Linux-derivative they use, and no industry standard exists, partly due to the fact that the differences in hardware architectures require changes to optimize the operating system to each hardware design. 10  3.1.2 Software Tools and Message Passing: The parallel architectures of supercomputers often dictate the use of special programming techniques to exploit their speed. Software tools for distributed processing include standard APIs such as MPI and PVM, VTL, and open source-based software solutions such as Beowulf. In the most common scenario, environments such as PVM and MPI for loosely connected clusters and Open MP for tightly coordinated shared memory machines are used. Significant effort is required to optimize an algorithm for the interconnect characteristics of the machine it will be run on; the aim is to prevent any of the CPUs from wasting time waiting on data from other nodes. GPGPUs have hundreds of processor cores and are programmed using programming models such as CUDA. Figure A.5: Technicians working on a cluster consisting of many computers working together by sending messages over a network. 3.2 Distributed Supercomputing: 11  3.2.1 Opportunistic Approaches: Opportunistic Supercomputing is a form of networked grid computing whereby a “super virtual computer” of many loosely coupled volunteer computing machines performs very large computing tasks. Grid computing has been applied to a number of large-scale embarrassingly parallel problems that require supercomputing performance scales. However, basic grid and cloud computing approaches that rely on volunteer computing can't handle traditional supercomputing tasks such as fluid dynamic simulations. Figure A.6: Example architecture of a grid computing system connecting many personal computers over the internet. The fastest grid computing system is the distributed computing project Folding@home. F@h reported 8.1 petaflops of x86 processing power as of March 2012. Of this, 5.8 petaflops are contributed by clients running on various GPUs, 1.7 petaflops come from PlayStation 3 systems, and the rest from various CPU systems. The BOINC platform hosts a number of distributed computing projects. As of May 2011, BOINC recorded a processing power of over 5.5 petaflops through over 480,000 active computers on the network[61] The most active project (measured by computational power), MilkyWay@home, reports processing power of over 700 teraflops through over 33,000 active computers. As of May 2011, GIMPS's distributed Mersenne Prime search currently achieves about 60 teraflops through over 25,000 registered computers. The 12 Internet PrimeNet Server supports GIMPS's grid computing approach, one of the earliest and most successful grid computing projects, since 1997. 3.2.3 Quasi-Opportunistic Approaches: Quasi-opportunistic Supercomputing is a form of distributed computing whereby the “super virtual computer” of a large number of networked geographically disperse computers performs huge processing power demanding computing tasks. Quasi-opportunistic supercomputing aims to provide a higher quality of service than opportunistic grid computing by achieving more control over the assignment of tasks to distributed resources and the use of intelligence about the availability and reliability of individual systems within the supercomputing network. However, quasi-opportunistic distributed execution of demanding parallel computing software in grids should be achieved through implementation of grid-wise allocation agreements, co-allocation subsystems, communication topology-aware allocation mechanisms, fault tolerant message passing libraries and data pre- conditioning. Figure A.7: Representation of an atmospheric model with differential equations that require supercomputing capabilities. 13