Depending on the application set, some machines were designed around problems or a single problem. Within a budget limit, the designer has to strike a balance between the number of nodes, amounts of memory, storage, and the type of interconnect. Using a slower less expensive interconnect e. Gigabit Ethernet allowed more compute nodes but, if a faster interconnect was needed e. InfiniBand , then the number of nodes was reduced.
Amounts of memory and storage also had to fit into a tunable budget-performance equation. The move away from SMPs and dedicated supercomputers was complete and continues today where the top systems are These initial cluster systems used single core processors often with two processor sockets per cluster node. There had been a steady increase in processor clock speed for each new generation, however processors were about to undergo a huge change.
Given these challenges, chip designers turned in another direction. Instead of making processors faster, they put more of them on a single processor substrate. The era of multi-core began with dual CPU modules cores sharing the resources of a single processor socket i. The new dual-core chips essentially doubled the number of CPU core elements in a cluster. As of June , eighty percent of the systems on the Top list have between cores per processor socket. The increase in computing density allowed both the number of HPC jobs capacity and the size of HPC jobs capability to increase.
As the numbers of cores increased, another technology began to migrate into HPC that accelerated performance for many applications. These systems were based on commodity Graphics Processing Units GPUs that contain large numbers hundreds to thousands of small efficient cores that worked in unison.
These types of operations, which are similar to those needed to render graphics, allowed processors to perform what is known as Single Instruction Multiple Data SIMD parallel processing. Like the switch to multi-core, not all application could take advantage of the new resources, but those that could, showed remarkable speed-up—often times faster. Like the previous epoch, elements of co-design are needed to standup an efficient clustered system. Depending on the application set, there were decisions as to the number of cores and memory per node and the need for GPU accelerators.
Indeed, due to the large amount of computing power available on a single node, certain applications that previously would span multiple nodes could now be run efficiently on one fat node. Thus, HPC cluster design would often take on a heterogeneous architecture and allow the workflow scheduler to direct applications to the appropriate resources within a cluster. Thirty five percent of the systems used 10 GbE, however, many of these clusters are not strictly HPC systems and are used for other purposes.
Unlike many HPC applications, the Top benchmark is not very sensitive to interconnect speed. While these systems have reached the petaFLOPs 10E15 Floating point operations per second level of computing and are responsible for producing many new discoveries, the current design may have difficulty pushing the HPC into the Exascale 10E18 regime.
Extending conventional approaches to this level is not expected to meet this goal. Though not a new concept1 , the co-design of hardware and software to meet a performance goal is the most encouraging approach.
Barrett et al. In particular, the Barrett paper defines the co-design approach as;. The co-design strategy is based on developing partnerships with computer vendors and application scientists and engaging them in a highly collaborative and iterative design process well before a given system is available for commercial use.
The process is built around identifying leading edge, high-impact scientific applications and providing concrete optimization targets rather than focusing on speeds and feeds FLOPs and bandwidth and percent of peak.
And, there will be an equal investment from the JU's participating countries, and as in-kind and cash contributions from its private partners. These investments will help accelerate the green and digital transitions and will contribute to sustainable recovery from the coronavirus pandemic and to EU resilience against future crises. They will support European researchers through fellowships, training and exchanges, build more connected and efficient European innovation ecosystems and create world-class research infrastructures.
India and EU leaders have agreed to build a sustainable and comprehensive Connectivity Partnership, as well as cooperate on supercomputers to bring solutions to challenges brought by the COVID pandemic, during the 16th EU-India Summit held on the 8th of May. High performance computing refers to computing systems with extremely high computational power that are able to solve hugely complex and demanding problems.
These computers should be among the top 5 in the world. The huge enabler here has been OpenCL. It has grown and matured to the point that it has its own market dynamics that behave differently from other enterprise, but also other high performance or scale-sensitive applications. The issue with FPGA technology is that it necessitates the optimisation of data movement and computation on an application-specific basis.
In a general-purpose HPC market that runs tens or even hundreds of applications across a cluster in a fairly short period, it is unlikely that HPC centres will want to switch off even a few nodes while they are optimised for a new application — let alone that entire cluster. They have also been best deployed in areas that are highly scalable and generally for applications that are text or integer-based.
The RISC reduced instruction set computing processor architecture was originally developed back in the s around a simplified instruction set that could potentially provide higher performance as it is capable of executing instructions using fewer microprocessor cycles per instruction. Developed by Sun Microsystems and introduced in , the first implementations of SPARC were based on bit operations and initially designed to be used in Sun Microsystems server and workstation systems, replacing Motorola processors.
SPARC international was eventually set up to license out the technology in the hopes of encouraging the development of the processor ecosystem. In HPC, the days are long over when users could just wait for the next CPU to deliver a 50 per cent increase in application performance. Now they must look to more innovative, architectural advances which require an understanding of parallelising code and how to map that efficiently to specific accelerator technologies.
So what we need is a family of solutions that covers this entire spectrum of applications and this is what we call the Amdahl spectrum. The future of HPC is no longer a monoculture of clusters of commodity hardware but rather a highly diverse ecosystem, populated by different processor technologies, different architecture, and different software solutions. It may be messy, but it will be interesting.
Robert Roe takes a look at a selection of HPC software tools that can help scientists create applications or run them more effectively. Robert Roe takes a look at a selection of software tools designed to make it easier to manage and implement HPC resources.
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