.

content/post/2017-06-cem.md

@@ -0,0 +1,21 @@
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+title = "CEM 2017"
+tags = []
+highlight = true
+math = false
+date = "2017-06-21T06:50:39-07:00"
+
+[header]
+  caption = ""
+  image = ""
+
++++
+
+I'm attending CEM 17 hosted at UPC Barcelona, Spain!
+
+<!--more-->
+
+![greet][greet] Collaborator and co-author Mert Hidayetoglu and I greeting conference attendees.
+
+[greet]: /img/201706_cem/FB_IMG_1498026827242.jpg "Greeting attendees."
+

content/post/gtc-2017.md

@@ -16,10 +16,10 @@ I've made my first trip to NVIDIA's GPU Technology Conference this year, to pres
 <!--more-->
 
 ![fast forward][ff] Here I am giving our fast-forward poster talk.
-![fast forward][monopoly] This is advertised as the world's largest monopolgy board.
-![fast forward][abdul] Abdul giving a 30-minute talk on RAI.
-![fast forward][poster] Our poster on display in the hall.
-![fast forward][entrance] A view of the entrance to the convention center.
+![monopoly][monopoly] This is advertised as the world's largest monopolgy board.
+![adbul][abdul] Abdul giving a 30-minute talk on RAI.
+![poster][poster] Our poster on display in the hall.
+![entrance][entrance] A view of the entrance to the convention center.
 
 [ff]: /img/gtc_2017/image1.jpg "The fast-forward Presentation"
 [monopoly]: /img/gtc_2017/IMG_20170508_110948.jpg "World's largest monopoly board"

content/publication/2017hidayetoglu.md

@@ -14,7 +14,7 @@ publication = "*Applied and Computational Electromagnetics Symposium, 2017.* For
 
 url_code = ""
 url_dataset = ""
-url_pdf = ""
+url_pdf = "pdf/2017aces-dbim.pdf"
 url_project = ""
 url_slides = ""
 url_video = ""

content/publication/2017pearson.md

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+draft = false
+
+date = "2017-06-22"
+title = "Comparative Performance Evaluation of Multi-GPU MLFMM Implementation for 2-D VIE Problems"
+authors = ["Carl Pearson", "Mert Hidayetoglu", "Wei Ren", "Weng Cho Chew", "Wen-Mei Hwu"]
+
+abstract = 'We compare multi-GPU performance of the multilevel
+fast multipole method (MLFMM) on two different systems:
+A shared-memory IBM S822LC workstation with four NVIDIA
+P100 GPUs, and 16 XK nodes (each is employed with a
+single NVIDIA K20X GPU) of the Blue Waters supercomputer.
+MLFMM is implemented for solving scattering problems involving
+two-dimensional inhomogeneous bodies. Results show that the
+multi-GPU implementation provides 794 and 969 times speedups
+on the IBM and Blue Waters systems over their corresponding
+sequential CPU executions, respectively, where the sequential
+execution on the IBM system is 1.17 times faster than on the
+Blue Waters System.'
+
+image = ""
+image_preview = ""
+math = false
+publication = "*Computing and Electromagnetics International Workshop.* IEEE, 2017."
+
+url_code = ""
+url_dataset = ""
+url_pdf = "pdf/mlfmm-cem2017.pdf"
+url_project = ""
+url_slides = ""
+url_video = ""
+
+selected = true
++++