Updated to add projects page properly

_config.yaml

@@ -1,6 +1,6 @@
 # site properties and page defaults
 title: ASU-IDI
-subtitle: 
+subtitle: The Intelligent Data Infrastructure Lab at Arizona State University
 description: An engaging 1-3 sentence description of your lab.
 header: images/background.jpg
 footer: images/background.jpg
@@ -30,6 +30,11 @@ defaults:
       type: "posts"
     values:
       layout: post
+  # markdown files in /_project_data
+  - scope:
+      type: "project_data"
+    values:
+      layout: post
 
 collections:
   # generate page for each member
@@ -38,6 +43,9 @@ collections:
   # generate page for each post
   posts:
     output: true
+  # generate page for each project post
+  project_data:
+    output: true
 
 # jekyll plugins
 plugins:

_includes/post-info.html

@@ -8,7 +8,6 @@
       {% include portrait.html lookup=include.author style="tiny" %}
     {% else %}
       <span data-tooltip="Author">
-        {% include icon.html icon="fa-solid fa-feather-pointed" %}
         <span>{{ include.author }}</span>
       </span>
     {% endif %}
@@ -33,5 +32,5 @@
 </div>
 
 {% if include.tags %}
-  {% include tags.html tags=include.tags link="blog" %}
+  {% include tags.html tags=include.tags link="projects" %}
 {% endif %}

_layouts/member.html

@@ -39,7 +39,7 @@
 </p> -->
 
 {% capture search -%}
-  blog/?search={{ page.name }}
+  projects/?search={{ page.name }}
 {%- endcapture %}
 
 <!--

_layouts/post.html

@@ -8,8 +8,6 @@ <h1 class="center">{{ page.title }}</h1>
   include post-info.html
   author=page.author
   member=page.member
-  published=page.date
-  updated=page.last_modified_at
   tags=page.tags
 %}
 

_project_data/gpt_project.md

@@ -0,0 +1,12 @@
+---
+title: LLM-Assisted Configuration Tuning for Log-Structured Merge-tree-based Key-Value Stores
+author: Madhumitha Sukumar, Jiaxin Dai, Kaushiki Singh, Vikriti Lokegaonkar, Viraj Thakkar, Zhichao Cao
+tags: LSM-KVS, Tuning, AI
+---
+
+<!-- A single line examplation of the project -->
+Design and develop an LLM-assisted auto-tuning framework for Log-Structured Merge-tree-based Key-Value Stores (LSM-KVS) to achieve better performance.
+
+Log-Structured Merge-tree-based Key-Value Stores (LSM-KVS) are widely used in today's IT infrastructure , and usually have over 100 options (e.g., HBase and RocksDB ) to tune performance for particular hardware (e.g., CPU, memory, and storage), software, and workloads (e.g., random, skewed, and read/write intensive) . However, tuning the LSM-KVS with appropriate configurations is always challenging, usually requiring IT professionals with LSM-KVS expertise to run hundreds of benchmarking evaluations. Existing related studies on LSM-KVS tuning solutions are still limited, lacking generality, adaptiveness to the versions and deployments. We believe the recent advancements of Large-Language-Models (LLMs) like OpenAI's GPT-4 can be a promising solution to achieve LSM-KVS auto-tuning: 1) LLMs are trained using collections of LSM-KVS-related blog, publications, and almost all the open-sourced code, which makes the LLMs a real "expert" of LSM-KVS; 2) LLMs has the strong inferential capability to analyze the benchmarking results and achieve automatic and interactive adjustments for LSM-KVS on particular hardware and workloads. However, how to design the auto-tuning framework based on LLMs and benchmarking tools, how to generate appropriate prompts for LLMs, and how to calibrate the unexpected errors and wrong configurations are three main challenges to be addressed.
+
+We propose to design and develop an LLM-assisted auto-tuning framework as shown in Figure with the following workflow: 1) Use default options file and a collection of system and hardware information as initial input. 2) Use a feedback loop with the LLM API and create new prompts for LLM with option changes and the processed benchmarking results in the previous iterations. 3) The newly generated options from LLM are calibrated (cleaned and corrected) for a new round of benchmarking; And 4) after several iterations, the benchmarking results have converged and it generates the final optimized option configurations. Note that the whole process is automatically deployed and executed without human intervention. We implemented the framework prototype on RocksDB v8.8.1 and OpenAI's GPT-4-1106 model, and open-sourced. Our preliminary evaluations show that with 5 iterations of auto-tuning, our framework achieves up to 20% of throughput improvement compared with default configurations.

projects/index.md

@@ -2,18 +2,19 @@
 title: Projects
 nav:
   order: 2
-  tooltip: Software, databases, and more
+  tooltip: Our Ideas, Solutions, and Work
   display: true
+  date: 2021-01-01
 ---
 
-# {% include icon.html icon="fa-solid fa-wrench" %}Projects
+# {% include icon.html icon="fa-solid fa-cubes" %}Projects
 
-{% include tags.html tags="publication, resource, website" %}
+{% include section.html %}
 
-{% include search-info.html %}
+{% include search-box.html %}
 
-{% include section.html %}
+{% include tags.html tags=site.tags %}
 
-## Current Projects
+{% include search-info.html %}
 
-{% include list.html data="project_posts" component="post-excerpt" %}
+{% include list.html data="project_data" component="post-excerpt" %}