Marketing Scoops: cloudcomputing

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Exascale computing is the latest milestone in cutting-edge supercomputers high-powered systems capable of processing calculations at speeds currently impossible using any other method. Exascale supercomputers are computers that run at the exaflop scale. The prefix “exa” denotes 1 quintillion, which is 1 x 10¹⁸ or a one with 18 zeroes after it. Flop stands for “Floating point operations per second,” a type of calculation used to benchmark computers for comparison purposes……..Continue reading….

By: Peter Ray Allison

Source: Live Science

Critics:

Throughout the decades, the management of heat density has remained a key issue for most centralized supercomputers. The large amount of heat generated by a system may also have other effects, e.g. reducing the lifetime of other system components. There have been diverse approaches to heat management, from pumping Fluorinert through the system, to a hybrid liquid-air cooling system or air cooling with normal air conditioning temperatures.

A typical supercomputer consumes large amounts of electrical power, almost all of which is converted into heat, requiring cooling. For example, Tianhe-1A consumes 4.04 megawatts (MW) of electricity. The cost to power and cool the system can be significant, e.g. 4 MW at $0.10/kWh is $400 an hour or about $3.5 million per year. Heat management is a major issue in complex electronic devices and affects powerful computer systems in various ways.

The thermal design power and CPU power dissipation issues in supercomputing surpass those of traditional computer cooling technologies. The supercomputing awards for green computing reflect this issue. The packing of thousands of processors together inevitably generates significant amounts of heat density that need to be dealt with. The Cray-2 was liquid cooled, and used a Fluorinert “cooling waterfall” which was forced through the modules under pressure.

However, the submerged liquid cooling approach was not practical for the multi-cabinet systems based on off-the-shelf processors, and in System X a special cooling system that combined air conditioning with liquid cooling was developed in conjunction with the Liebert company. In the Blue Gene system, IBM deliberately used low power processors to deal with heat density. The IBM Power 775, released in 2011, has closely packed elements that require water cooling.

The IBM Aquasar system uses hot water cooling to achieve energy efficiency, the water being used to heat buildings as well. The energy efficiency of computer systems is generally measured in terms of “FLOPS per watt”. In 2008, Roadrunner by IBM operated at 376 MFLOPS/W. In November 2010, the Blue Gene/Q reached 1,684 MFLOPS/W and in June 2011 the top two spots on the Green 500 list were occupied by Blue Gene machines in New York (one achieving 2097 MFLOPS/W) with the DEGIMA cluster in Nagasaki placing third with 1375 MFLOPS/W.

Because copper wires can transfer energy into a supercomputer with much higher power densities than forced air or circulating refrigerants can remove waste heat, the ability of the cooling systems to remove waste heat is a limiting factor. As of 2015, many existing supercomputers have more infrastructure capacity than the actual peak demand of the machine – designers generally conservatively design the power and cooling infrastructure to handle more than the theoretical peak electrical power consumed by the supercomputer.

Designs for future supercomputers are power-limited – the thermal design power of the supercomputer as a whole, the amount that the power and cooling infrastructure can handle, is somewhat more than the expected normal power consumption, but less than the theoretical peak power consumption of the electronic hardware. Since the end of the 20th century, supercomputer operating systems have undergone major transformations, based on the changes 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. Since 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 full Linux distribution 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 deal with inevitable hardware failures when tens of thousands of processors are present.

Although most modern supercomputers use Linux-based operating systems, each manufacturer has its own specific Linux distribution, 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. 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 software such as Beowulf. In the most common scenario, environments such as PVM and MPI for loosely connected clusters and OpenMP 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 or OpenCL. Moreover, it is quite difficult to debug and test parallel programs. Special techniques need to be used for testing and debugging such applications. Cloud computing with its recent and rapid expansions and development have grabbed the attention of high-performance computing (HPC) users and developers in recent years.

Cloud computing attempts to provide HPC-as-a-service exactly like other forms of services available in the cloud such as software as a service, platform as a service, and infrastructure as a service. HPC users may benefit from the cloud in different angles such as scalability, resources being on-demand, fast, and inexpensive. On the other hand, moving HPC applications have a set of challenges too. Good examples of such challenges are virtualization overhead in the cloud, multi-tenancy of resources, and network latency issues.

Much research is currently being done to overcome these challenges and make HPC in the cloud a more realistic possibility. In 2016, Penguin Computing, Parallel Works, R-HPC, Amazon Web Services, Univa, Silicon Graphics International, Rescale, Sabalcore, and Gomput started to offer HPC cloud computing. The Penguin On Demand (POD) cloud is a bare-metal compute model to execute code, but each user is given virtualized login node. POD computing nodes are connected via non-virtualized 10 Gbit/s Ethernet or QDR InfiniBand networks.

User connectivity to the POD data center ranges from 50 Mbit/s to 1 Gbit/s. Citing Amazon’s EC2 Elastic Compute Cloud, Penguin Computing argues that virtualization of compute nodes is not suitable for HPC. Penguin Computing has also criticized that HPC clouds may have allocated computing nodes to customers that are far apart, causing latency that impairs performance for some HPC applications.

Supercomputers generally aim for the maximum in capability computing rather than capacity computing. Capability computing is typically thought of as using the maximum computing power to solve a single large problem in the shortest amount of time. Often a capability system is able to solve a problem of a size or complexity that no other computer can, e.g. a very complex weather simulation application.

Capacity computing, in contrast, is typically thought of as using efficient cost-effective computing power to solve a few somewhat large problems or many small problems. Architectures that lend themselves to supporting many users for routine everyday tasks may have a lot of capacity but are not typically considered supercomputers, given that they do not solve a single very complex problem.

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