As part of our research in Phylanx, our team has worked to create GPU support in Blaze. In this seminar Jules Pénchot, an intern from Université Paris-Saclay, presents his library, Blaze CUDA. Continue reading
Tag: C++
Compiling and Running Blazemark
By Shahrzad Shirzad
Blazemark is the benchmark suite for Blaze library. In order to compile and run Blazemark with HPX backend, take the following steps: Continue reading
Phylanx Seminar: Plugging into the Power of Phylanx II
In this week’s seminar, Hartmut returns to the implementation of primitives; this time in the form of a Phylanx plugin. Hartmut creates the primitive “constants_of_nature” which returns the value of e, pi, or Continue reading
Phylanx Seminar: Plugging into the Power of Phylanx
On Thursday, Hartmut demonstrated how the Phylanx project implements primitives by creating an add “primitive” example independent Phylanx. He started with an empty file and built a small model of Continue reading
Phylanx Seminar: Revealing the Magic of Blaze
Over the past several weeks, many team members have been asking how aspects of the Phylanx project are implemented. In this seminar, Hartmut explains the techniques used by Blaze to optimize matrix operations. He implements a matrix addition example which uses types, templates, and curiously recurring template patterns (CRTP) to reduce the number of temporaries made during the execution of the code. By avoiding these extra allocations in a compiler friendly way, Blaze can drastically reduce the amount of time it takes perform matrix operations. You can find links to the seminar materials below:
Seminar Video: https://www.youtube.com/watch?v=F5E8cOqHmRU
Naive Matrix Implementation: http://stellar.cct.lsu.edu/files/phylanx_seminars/03.21.18_seminar_blaze_magic/naive_matrix_03.21.18.cpp
Optimized Matrix Implementation: http://stellar.cct.lsu.edu/files/phylanx_seminars/03.21.18_seminar_blaze_magic/optimized_matrix_03.21.18.cpp
Introduction to Phylanx Coding
In this post I’ll go through the simple implementation of LRA (Logistic Regression Algorithm) to outline the Phylanx architecture and also demonstrate how one might go about writing their own programs in Phylanx. The complete version of the code discussed in this post can be found in the project’s GitHub repository under examples/algorithms directory and the corresponding dataset can be found here. Continue reading