From High-Level Specification to High-Performance Code
Volume 106, Issue 11 | November 2018
Guest Editors:
Special Issue Papers
Scanning the Issue
By F. Franchetti, J. M. F. Moura, D. A. Padua, and J. Dongarra
Machine Learning in Compiler Optimization
By Z. Wang and M. O’Boyle
This paper discusses machine-learning-based compilation techniques, which have become mainstream.
Domain-Specific Optimization and Generation of High-Performance GPU Code for Stencil Computations
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.
The Sparse Polyhedral Framework: Composing Compiler-Generated Inspector-Executor Code
By M. M. Strout, M. Hall, and C. Olschanowsky
This paper discusses an inspector–executor approach for sparse polyhedral programs.
SPIRAL: Extreme Performance Portability
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.
Automating the Development of High-Performance Multigrid Solvers
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.
The Long and Winding Road Toward Efficient High-Performance Computing
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.
The Ongoing Evolution of OpenMP
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.
Navigating the Landscape for Real-Time Localization and Mapping for Robotics and Virtual and Augmented Reality
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.
Autotuning Numerical Dense Linear Algebra for Batched Computation With GPU Hardware Accelerators
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.
Japanese Autotuning Research: Autotuning Languages and FFT
By T. Katagiri and D. Takahashi
This paper discusses the Japanese automatic performance tuning research landscape.
Autotuning in High-Performance Computing Applications
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.
Editorial
Point of View
Data Transparency: Concerns and Prospects
By N. Laoutaris
