Reaching new markets Industry stands to benefit enormously from wider application of HPC, but although HPC has been exploited in many industrial applications areas, there are still many others where it has great potential but as yet little penetration. HPC has historically succeeded in scientific, engineering and manufacturing applications where high performance by itself is sufficient to provide a solution. There are many application areas however where high performance alone is not sufficient. Non-functional aspects of computing such as reliability, robustness and security are needed in application areas such as real-time financial trading, intelligent transport and the management of utilities. The HPC community must adapt to meet these types of requirements if new markets and applications are to benefit from it. There are clear incentives for both the HPC supply and application demand sides to meet these requirements. For the suppliers, untapped markets will provide a revenue stream for hardware and software providers. On the demand side there are the benefits that HPC has been proven to deliver in other sectors. The HPC community must also get better at selling HPC as an enabling technology that can achieve results in industrial applications, not as something that should be used because it is intrinsically clever or advanced. HPC must also be seen to deliver value beyond academic research. PlanetHPC encountered a view expressed by many industrialists that HPC was not a technology for them. Many failed to see its potential and so lacked interest. HPC must be promoted intelligently so as to raise awareness of opportunity while avoiding the pitfalls of hype. Dealing with massive data
There also tends to be a reliance on people who have combined expertise in both HPC and specific applications. For example, an engineer working on a computational fluid problem often needs detailed knowledge of how the software they are using works, and how its performance can be optimised on particular machines or for certain data sets. This is not an optimum use of skills, and places too much reliance on key individuals. Key recommendations Based on discussions with leading industry and academic HPC experts, the PlanetHPC project makes a number of key recommendations related to these four challenges: Key recommendation 1: Carry out research into new simulation and numerical methods that scale to millions of processors and exploit datalocality. Encourage co-design with processor development. Key recommendation 2: Develop programming models that can be efficiently implemented on massively parallel and heterogeneous systems, including emerging low-power computing platforms. Key recommendation 3: Investigate the migration of existing codes to accelerator and multicore technologies, and developing re-usable libraries and software modules optimised for these architectures. Key recommendation 4: Demonstrate to innovative software companies the need to modernise applications and to engage in research programmes. Ensure that collaboration models protect the intellectual property of software vendors. Key recommendation 5: Promote the image of HPC as a key enabling technology, particularly aiming at SMEs; dispel the image that HPC is only for technically-knowledgeable large companies.
There is a relentless trend of massive data generation across all human activities: business, government, personal and academic. It is estimated that the amount of data produced each year is greater than the sum of all that previously created. The growth of data has outpaced the development of tools to deal with it. This has left us struggling, not only with the sheer volume of data being generated, but also with the ever-increasing complexity of data interactions.
Key recommendation 6: Understand non-functional requirements of new application areas.
This is a significant issue for HPC in terms of applications and systems, and it impacts in a number of ways. Computing systems have typically been designed for compute-intensive workloads, but different approaches to system design are needed for data-intensive workloads. New tools and methods are needed to manage, curate and extract value from data. This is important across the ICT domain, but is of particular significance for HPC applications which typically generate large data sets.
Key recommendation 9: Develop tools for managing data on a massive scale and extracting value from it; the HPC community should engage with those involved in big-data research.
Developing skills
Key recommendation 7: Make it easier for small companies to evaluate HPC technology; establish a network of HPC pilot projects. Key recommendation 8: Investigate system architectures suitable for dataintensive computing.
Key recommendation 10: Make training and education an essential part of infrastructure and research initiatives. Key recommendation 11: Adopt an inter-disciplinary approach in tackling research challenges; involve application specialists, mathematicians and software engineers in R&D&I activities.
PlanetHPC has identified a shortage in HPC skills, with only a very small fraction of the ICT workforce having HPC and parallel computing expertise or experience. 6
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