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LLNL · Nov 5, 2024 · 395f4f8 · 395f4f8
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diff --git a/docs/composing_parallel_sorting_data.ipynb b/docs/composing_parallel_sorting_data.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Composing Parallel Sorting Data - (Part A) Composing & Modeling Parallel Sorting Performance Data: Thicket Tutorial\n",
+    "# PPAM '24: Composing & Modeling Parallel Sorting Performance Data (Part A): Thicket Tutorial\n",
     "\n",
     "The parallel sorting dataset consists of 8,747 MPI sorting algorithm performance profiles (collected with [Caliper](https://software.llnl.gov/Caliper/)) for 5 different algorithms and 51 implementations.\n",
     "We start with a dataset that includes over 10,000 performance profiles, and we show how to apply various filters and checks on the performance data to remove profiles that do not match our criteria.\n",

diff --git a/docs/modeling_parallel_sorting_data.ipynb b/docs/modeling_parallel_sorting_data.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Modeling Parallel Sorting Data - (Part B) Composing & Modeling Parallel Sorting Performance Data: Thicket Tutorial\n",
+    "# PPAM '24: Composing & Modeling Parallel Sorting Performance Data (Part B): Thicket Tutorial\n",
     "\n",
     "In part B, we use machine learning to predict the parallel algorithm class from the performance data we processed and composed in part A. Running notebook `08A_composing_parallel_sorting_data.ipynb` is necessary to generate the data that we will use in this notebook.\n",
     "\n",

diff --git a/docs/thicket_rajaperf_clustering.ipynb b/docs/thicket_rajaperf_clustering.ipynb
@@ -14,7 +14,7 @@
     "tags": []
    },
    "source": [
-    "# Clustering RAJA Performance Suite Dataset: Thicket Tutorial\n",
+    "# HPDC '23: Optimization-Based K-means Clustering on the RAJA Performance Suite: Thicket Tutorial\n",
     "\n",
     "Thicket is a python-based toolkit for Exploratory Data Analysis (EDA) of parallel performance data that enables performance optimization and understanding of applications’ performance on supercomputers. It bridges the performance tool gap between being able to consider only a single instance of a simulation run (e.g., single platform, single measurement tool, or single scale) and finding actionable insights in multi-dimensional, multi-scale, multi-architecture, and multi-tool performance datasets.\n",
     "\n",

diff --git a/docs/tma_speedup_clustering.ipynb b/docs/tma_speedup_clustering.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# RAJA Performance Suite Analysis: Thicket Tutorial\n",
+    "# P3HPC '24: Top Down and Hierarchical Clustering on the RAJA Performance Suite: Thicket Tutorial\n",
     "\n",
     "In this notebook, we compose performance data from experiments using the RAJA Performance Suite on various hardware:\n",
     "\n",