Comment duplicate publist entry

Author: Chaitanya-Shahare

_data/publist.yml

@@ -399,24 +399,24 @@
   url: https://arxiv.org/abs/2304.02650
   year: '2023'
 
-- title: Fast And Automatic Floating Point Error Analysis With CHEF-FP
-  author: Garima Singh, Baidyanath Kundu, Harshitha Menon, Alexander Penev, David J. Lange,
-    Vassil Vassilev
-  abstract: |
-    As we reach the limit of Moore's Law, researchers are exploring different paradigms to
-    achieve unprecedented performance. Approximate Computing (AC), which relies on the ability
-    of applications to tolerate some error in the results to trade-off accuracy for performance,
-    has shown significant promise. Despite the success of AC in domains such as Machine Learning,
-    its acceptance in High-Performance Computing (HPC) is limited due to stringent requirements
-    for accuracy. We need tools and techniques to identify regions of code that are amenable to
-    approximations and their impact on the application output quality to guide developers to employ
-    selective approximation. To this end, we propose CHEF-FP, a flexible, scalable, and easy-to-use
-    source-code transformation tool based on Automatic Differentiation (AD) for analyzing
-    approximation errors in HPC applications. CHEF-FP uses ...
-  cites: '0'
-  eprint: https://arxiv.org/abs/2304.06441
-  url: https://arxiv.org/abs/2304.06441
-  year: '2023'
+# - title: Fast And Automatic Floating Point Error Analysis With CHEF-FP
+#   author: Garima Singh, Baidyanath Kundu, Harshitha Menon, Alexander Penev, David J. Lange,
+#     Vassil Vassilev
+#   abstract: |
+#     As we reach the limit of Moore's Law, researchers are exploring different paradigms to
+#     achieve unprecedented performance. Approximate Computing (AC), which relies on the ability
+#     of applications to tolerate some error in the results to trade-off accuracy for performance,
+#     has shown significant promise. Despite the success of AC in domains such as Machine Learning,
+#     its acceptance in High-Performance Computing (HPC) is limited due to stringent requirements
+#     for accuracy. We need tools and techniques to identify regions of code that are amenable to
+#     approximations and their impact on the application output quality to guide developers to employ
+#     selective approximation. To this end, we propose CHEF-FP, a flexible, scalable, and easy-to-use
+#     source-code transformation tool based on Automatic Differentiation (AD) for analyzing
+#     approximation errors in HPC applications. CHEF-FP uses ...
+#   cites: '0'
+#   eprint: https://arxiv.org/abs/2304.06441
+#   url: https://arxiv.org/abs/2304.06441
+#   year: '2023'
 
 - title: Efficient and Accurate Automatic Python Bindings with cppyy & Cling
   author: Baidyanath Kundu, Vassil Vassilev, Wim Lavrijsen
@@ -486,5 +486,6 @@
   pages: 1018-1028
   publisher: IEEE
   url: https://ieeexplore.ieee.org/document/10177445
+  link: /publications/fast-and-automatic-floating-point-error-analysis-with-chef-fp
   volume: '608'
-  year: '2023'
+  year: '2023'