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Foreword to the Special Issue of the Twenty Sixth
International Heterogeneity in Computing Workshop
(HCW) and to the Fifteenth International Workshop on
Algorithms, Models and Tools for Parallel Computing
on Heterogeneous Platforms (HeteroPar)

Jorge G. Barbosa, Emmanuel Jeannot

► To cite this version:

Jorge G. Barbosa, Emmanuel Jeannot. Foreword to the Special Issue of the Twenty Sixth International Heterogeneity in Computing Workshop (HCW) and to the Fifteenth International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (HeteroPar). Concurrency and Computation: Practice and Experience, 2018, pp.2. 10.1002/cpe.5007 . hal-01903118

HAL Id: hal-01903118

https://inria.hal.science/hal-01903118

Submitted on 24 Oct 2018

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Foreword to the Special Issue of the Twenty Sixth International Heterogeneity in
Computing Workshop (HCW) and to the Fifteenth International Workshop on
Algorithms, Models and Tools for Parallel Computing on Heterogeneous
Platforms (HeteroPar)

Jorge G. Barbosa¹Emmanuel Jeannot² and

¹LIACC, Faculdade de Engenharia, Universidade do Porto, Portugal

²Inria, LaBRI, Univ. of Bordeaux, CNRS, Talence, France

Heterogeneity is emerging as one of the most profound and challenging characteristics of today's parallel environments. As most modern computing systems are heterogeneous, from the macro level, where networks of distributed computers, composed by diverse node architectures, are interconnected with potentially heterogeneous networks, to the micro level, where deeper memory hierarchies and various accelerator architectures are increasingly common, the impact of heterogeneity on all computing tasks is increasing rapidly.

Traditional parallel algorithms, programming environments and tools, designed for legacy homogeneous multiprocessors, will at best achieve a small fraction of the efficiency and the potential performance that we should expect from parallel computing in tomorrow's highly diversified and mixed environments. New ideas, innovative algorithms, and specialized programming environments and tools are needed to efficiently use these new and multifarious parallel architectures.

This special issue has the purpose to collect extended version of contributions submitted to the Twenty Sixth International Heterogeneity in Computing Workshop (HCW) and to the Fifteenth International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (HeteroPar). After a thorough peer-reviewing process, three papers were selected for publication. The topics addressed in this issue are the performance of cloud function providers, a cross-architecture Kalman filter to accelerate the reconstruction computation of particles collision and, a methodology to model applications jointly with a fast and greedy algorithm to obtain realist mapping and scheduling solution on heterogeneous systems.

Cloud functions are becoming an increasingly popular method of running distributed applications, as they allow the developer for deploying their code in the form of a function to the cloud, which is then responsible for automatic resource provision and scaling. Kamil Figiela, Adam Gajek, Adam Zima, Beata Obrok and Maciej Malawski [Figiela, 2018] present a performance evaluation study of heterogeneous cloud functions where the major cloud function providers are evaluated, namely, AWS Lambda, Azure Functions, Google Cloud Functions and IBM Cloud Functions.

At the LHCb detector in the Large Hadron Collider, the reconstruction of particle collisions in high-energy physics detectors happens at an average rate of 30 million times per second, being the Kalman filter a fundamental element in this process. Due to iterative enhancements in the detector's technology, together with the projected removal of the hardware filter, the rate of particles that will need to
be processed in software in real-time is expected to increase in the coming years
by a factor of 40. To tackle such a huge amount of data, Daniel Pérez and Omar
Awile [Pérez, 2018], present a cross-architecture Kalman filter optimized for low-
rank problems and modern SIMD architectures. Their implementation increases
the throughput of the Kalman filter sequences, achieving a performance gain on
existing hardware.

Nowadays, numerous services need the execution of complex applications, such
as weather forecasting, search engines, big data medical analyzes or intelligent
transportation systems. A large amount of energy is used to power them, and it is
of primary importance to compute more efficiently to sustain the increasing
demand of computing power while keeping energy consumption reasonable. As
these applications are heterogeneous in terms of the computational paradigm,
heterogeneous resources are seen as more adapted to efficiently execute parts of
applications. Nevertheless, the exploitation of these platforms raises new
challenges in terms of application management optimization. To tackle this
problem, Lilia Zaourar, Massinissa Ait Aba, David Briand and Jean-Marc Philippe
[Zaourar, 2018], propose a detailed modeling of the applications and the
underlying hardware to be able to find realistic solutions. Two implementations
are provided with state-of-the-art tools in order to compare the mapping and
scheduling solutions to the fast greedy online resolution algorithm proposed in
the paper.

These papers hence represent top of the shelf research results and we hope that
you will find them informative, interesting and inspiring.

## REFERENCES

[Figiela, 2018] Kamil Figiela, Adam Gajek, Adam Zima, Beata Obrok and Maciej Malawski, *Performance Evaluation of Heterogeneous Cloud Functions*, Concurrency and Computation: Practice and Experience [this issue].

[Pérez, 2018] Daniel Pérez and Omar Awile, *An Efficient Low-Rank Kalman Filter for Modern SIMD Architectures*, Concurrency and Computation: Practice and Experience [this issue].

[Zaourar, 2018] Lilia Zaourar, Massinissa Ait Aba, David Briand and Jean-Marc Philippe, *Task management on fully heterogeneous micro-server system: modeling and resolution strategies*, Concurrency and Computation: Practice and Experience [this issue].