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proceedings of the ieee cover nov 2018
proceedings of the ieee cover nov 2018

Special Issue: From High-Level Specification to High-Performance Code

Volume 106, Issue 11

November 2018

Guest Editors

Special Issue Papers

By F. Franchetti, J. M. F. Moura, D. A. Padua, and J. Dongarra

By Z. Wang and M. O’Boyle

This paper discusses machine-learning-based compilation techniques, which have become mainstream.

By P. S. Rawat, M. Vaidya, A. Sukumaran-Rajam, M. Ravishankar, V. Grover, A. Rountev, L.-N. Pouchet, and P. Sadayappan

This paper discusses the compilation of a domain-specific language used to target graphics processors.

By M. M. Strout, M. Hall, and C. Olschanowsky

This paper discusses an inspector–executor approach for sparse polyhedral programs.

By F. Franchetti, T. M. Low, D. T. Popovici, R. M. Veras, D. G. Spampinato, J. R. Johnson, M. Püschel, J. C. Hoe, and J. M. F. Moura

This paper provides an end-to-end discussion of the SPIRAL system, its domain-specific languages, and code generation techniques.

By C. Schmitt, S. Kronawitter, F. Hannig, J. Teich, and C. Lengauer

This paper discusses domain-specific languages and code generation targeting stencil computations in the context of the German ExaStencil effort.

By W. Jalby, D. Kuck, A. D. Malony, M. Masella, A. Mazouz, and M. Popov

This paper provides a mainly European perspective on the road to ExaScale.

By B. R. de Supinski, T. R. W. Scogland, A. Duran, M. Klemm, S. Mateo Bellido, S. L. Olivier, C. Terboven, and T. G. Mattson

This paper discusses the OpenMP framework’s past, current status, and anticipated future in the face of the evolving CPU and accelerator landscape.

By S. Saeedi, B. Bodin, H. Wagstaff, A. Nisbet, L. Nardi, J. Mawer, N. Melot, O. Palomar, E. Vespa, T. Spink, C. Gorgovan, A. Webb, J. Clarkson, E. Tomusk, T. Debrunner, K. Kaszyk, P. Gonzalez-De-Aledo, A. Rodchenko, G. Riley, C. Kotselidis, B. Franke, M. F. P. O’Boyle, A. J. Davison, P. H. J. Kelly, M. Luján, and S. Furber

This paper shows for the important example of simultaneous localization and mapping (SLAM) the compilation and tuning techniques necessary to reach high performance.

By J. Dongarra, M. Gates, J. Kurzak, P. Luszczek, and Y. M. Tsai

This paper discusses automatic performance tuning for small linear algebra kernels, which are important building blocks in many engineering and science applications.

By T. Katagiri and D. Takahashi

This paper discusses the Japanese automatic performance tuning research landscape.

By P. Balaprakash, J. Dongarra, T. Gamblin, M. Hall, J. K. Hollingsworth, B. Norris, and R. Vuduc

This paper discusses how to make automatic performance tuning a standard technique for high-performance computing applications.


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