Changing computing paradigms towards power efficiency.
Power awareness is fast becoming immensely important in computing, ranging from the traditional high-performance computing applications to the new generation of data centric workloads. In this work, we describe our efforts towards a power-efficient computing paradigm that combines low- and high-precision arithmetic. We showcase our ideas for the widely used kernel of solving systems of linear equations that finds numerous applications in scientific and engineering disciplines as well as in large-scale data analytics, statistics and machine learning. Towards this goal, we developed tools for the seamless power profiling of applications at a fine-grain level. In addition, we verify here previous work on post-FLOPS/W metrics and show that these can shed much more light in the power/energy profile of important applications.
Faculty of Mathematics and Physics, Computer Science Institute, Charles University in Prague, Malostranské nám. 25, 118 00 Prague, Czech Republic.
Pavel
P
Klavík
IBM Research-Zurich, Säumerstrasse 4, 8803 Rüschlikon, Switzerland.
A Cristiano I
AC
Malossi
IBM Research-Zurich, Säumerstrasse 4, 8803 Rüschlikon, Switzerland bek@zurich.ibm.com.
Costas
C
Bekas
IBM Research-Zurich, Säumerstrasse 4, 8803 Rüschlikon, Switzerland.
Alessandro
A
Curioni
Cholesky method; conjugate gradient method; energy-aware computing; performance metrics; power consumption