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Add a minicon page to CompRes website #337

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14 changes: 13 additions & 1 deletion .github/actions/spelling/allow/terms.txt
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VVCR
VVLLVM
VVMODE
VVSNL
VVSNL
Autodiff
cladtorch
FASTQ
firstprivate
godbolt
KWh
lastprivate
markdownify
crcon
crconlist
petabyte
Vkv

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197 changes: 197 additions & 0 deletions _data/crconlist2025.yml
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- name: "CompilerResearchCon 2025 (day 2)"
date: 2025-11-13 15:00:00 +0200
time_cest: "15:00"
connect: "[Link to zoom](https://princeton.zoom.us/j/94431046845?pwd=D5i77Qb0PgfwwIubvbo2viEunne7eQ.1)"
label: gsoc2025_wrapup_2
agenda:
- title: "Implementing Debugging Support for xeus-cpp"
speaker:
name: "Abhinav Kumar"
time_cest: "15:00 - 15:20"
description: |
This proposal outlines integrating debugging into the xeus-cpp kernel
for Jupyter using LLDB and its Debug Adapter Protocol (lldb-dap).
Modeled after xeus-python, it leverages LLDB’s Clang and JIT debugging
support to enable breakpoints, variable inspection, and step-through
execution. The modular design ensures compatibility with Jupyter’s
frontend, enhancing interactive C++ development in notebooks.

This project achieved DAP protocol integration with xeus-cpp. User can
use the JupyterLab’s debugger panel to debug C++ JIT code. Applying and
hitting breakpoints, stepping in and out of functions are supported in
xeus-cpp. Additionally, during this project I had refactored
the Out-of-Process JIT execution which was the major part in implementing
the debugger.


# slides: /assets/presentations/...

- title: "Activity analysis for reverse-mode differentiation of (CUDA) GPU kernels"
speaker:
name: "Maksym Andriichuk"
time_cest: "15:20 - 15:40"
description: |
Clad is a Clang plugin designed to provide automatic differentiation (AD) for C++
mathematical functions. It generates code for computing derivatives modifying
Abstract-Syntax-Tree(AST) using LLVM compiler features. It performs advanced program
optimization by implementing more sophisticated analyses because it has access to a
rich program representation – the Clang AST.

The project achieved to optimize code that contains potential data-race conditions,
significantly speeding up the execution. Thread Safety Analysis is a static analysis
that detects possible data-race conditions that would enable reducing atomic
operations in the Clad-produced code.

# slides: /assets/presentations/...

- title: "Enable automatic differentiation of OpenMP programs with Clad"
speaker:
name: "Jiayang Li"
time_cest: "15:40 - 16:00"
description: |
This project extends Clad, a Clang-based automatic differentiation tool for C++, to
support OpenMP programs. This project enables Clad to parse and differentiate
functions with OpenMP directives, thereby enabling gradient computation in
multi-threaded environments.

This project achieved Clad support for both forward and reverse mode differentiation
of common OpenMP directives (parallel, parallel for) and clauses (private,
firstprivate, lastprivate, shared, atomic, reduction) by implementing OpenMP-related
AST parsing and designing corresponding differentiation strategies. Additional
contributions include example applications and comprehensive tests.


# slides: /assets/presentations/...

- title: "Using ROOT in the field of Genome Sequencing"
speaker:
name: "Aditya Pandey"
time_cest: "16:00 - 16:20"
description: |
The project extends ROOT, CERN's petabyte-scale data processing framework, to address
the critical challenge of managing genomic data that generates upto 200GB per human
genome. By leveraging ROOT's big data expertise and introducing the next-generation
RNTuple columnar storage format specifically optimized for genomic sequences, the
project eliminates the traditional trade-off between compression efficiency and
access speed in bioinformatics.

The project achieved comprehensive genomic data support through validating GeneROOT
baseline performance benchmarks against BAM/SAM formats, implementing RNTuple-based
RAM (ROOT Alignment Maps) format with full SAM/BAM field support and smart reference
management, demonstrating 23.5% smaller file sizes compared to CRAM while delivering
1.9x faster large region queries and 3.2x faster full chromosome scans, optimizing
FASTQ compression from 14.2GB to 6.8GB. We also developed chromosome based
file-splitting for larger genome file so that chromosome based data can be extracted.


# slides: /assets/presentations/...

- name: "CompilerResearchCon 2025 (day 1)"
date: 2025-10-30 15:00:00 +0200
time_cest: "15:00"
connect: "[Link to zoom](https://princeton.zoom.us/j/94431046845?pwd=D5i77Qb0PgfwwIubvbo2viEunne7eQ.1)"
label: gsoc2025_wrapup_1
agenda:
- title: "CARTopiaX an Agent-Based Simulation of CAR -T -Cell Therapy built on BioDynaMo"
speaker:
name: "Salvador de la Torre Gonzalez"
time_cest: "15:00 - 15:20"
description: |
CAR- T-cell therapy is a form of cancer immunotherapy that engineers a
patient’s T cells to recognize and eliminate malignant cells. Although
highly effective in leukemias and other hematological cancers, this therapy
faces significant challenges in solid tumors due to the complex and
heterogeneous tumor microenvironment. CARTopiaX is an advanced agent-based
model developed to address this challenge, using the mathematical framework
proposed in the Nature paper “In silico study of heterogeneous tumour-derived
organoid response to CAR T-cell therapy,” successfully replicating its core
results. Built on BioDynaMo, a high-performance, open-source platform for
large-scale and modular biological modeling, CARTopiaX enables detailed
exploration of complex biological interactions, hypothesis testing, and
data-driven discovery within solid tumor microenvironments.

The project achieved major milestones, including simulations that run more than
twice as fast as previous model, allowing rapid scenario exploration and robust
hypothesis validation; high-quality, well-structured, and maintainable C++ code
developed following modern software engineering principles; and a scalable,
modular, and extensible architecture that fosters collaboration, customization,
and the continuous evolution of an open-source ecosystem. Altogether, this work
represents a meaningful advancement in computational biology, providing
researchers with a powerful tool to investigate CAR- T- cell dynamics in solid
tumors and accelerating scientific discovery while reducing the time and cost
associated with experimental wet-lab research.

# slides: /assets/presentations/...

- title: "Efficient LLM Training in C++ via Compiler-Level Autodiff with Clad"
speaker:
name: "Rohan Timmaraju"
time_cest: "15:20 - 15:40"
description: |
The computational demands of Large Language Model (LLM) training are
often constrained by the performance of Python frameworks. This project
tackles these bottlenecks by developing a high-performance LLM training
pipeline in C++ using Clad, a Clang plugin for compiler-level automatic
differentiation. The core of this work involved creating cladtorch, a new
C++ tensor library with a PyTorch-style API designed for compatibility
with Clad's differentiation capabilities. This library provides a more
user-friendly interface for building and training neural networks while
enabling Clad to automatically generate gradient computations for
backpropagation.

Throughout the project, I successfully developed two distinct LLM training
implementations. The first, using the cladtorch library, established a
functional and flexible framework for Clad-driven AD. To further push
performance boundaries, I then developed a second, highly-optimized
implementation inspired by llm.c, which utilizes pre-allocated memory buffers
and custom kernels. This optimized C-style approach, when benchmarked for
GPT-2 training on a multithreaded CPU, outperformed the equivalent PyTorch
implementation. This work successfully demonstrates the viability and
performance benefits of compiler-based AD for deep learning in C++ and
provides a strong foundation for future hardware acceleration, such as porting
the implementation to CUDA.

# slides: /assets/presentations/...

- title: "Implement and improve an efficient, layered tape with prefetching capabilities"
speaker:
name: "Aditi Milind Joshi"
time_cest: "15:40 - 16:00"
description: |
Clad relies on a tape data structure to store intermediate values during reverse
mode differentiation. This project focuses on enhancing the core tape implementation
in Clad to make it more efficient and scalable. Key deliverables include replacing
the existing dynamic array-based tape with a slab allocation approach and small
buffer optimization, enabling multilayer storage, and introducing thread safety to
support concurrent access.

The current implementation replaces the dynamic array with a slab-based structure
and a small static buffer, eliminating costly reallocations. Thread-safe access
functions have been added through a mutex locking mechanism, ensuring safe parallel
tape operations. Ongoing work includes developing a multilayer tape system with
offloading capabilities, which will allow only the most recent slabs to remain in
memory.


# slides: /assets/presentations/...

- title: "Support usage of Thrust API in Clad"
speaker:
name: "Abdelrhman Elrawy"
time_cest: "16:00 - 16:20"
description: |
This project integrates NVIDIA's Thrust library into Clad, a Clang-based automatic
differentiation tool for C++. By extending Clad's source-to-source transformation
engine to recognize and differentiate Thrust parallel algorithms, the project
enables automatic gradient generation for GPU-accelerated scientific computing
and machine learning applications.

The project achieved Thrust support in Clad through implementing custom derivatives
for core algorithms including thrust::reduce, thrust::transform,
thrust::transform_reduce, thrust::inner_product, thrust::copy, scan operations
(inclusive/exclusive), thrust::adjacent_difference, and sorting primitives.
Additional contributions include Thrust data containers like thrust::device_vector,
generic functor handling for transformations, demonstration applications, and
comprehensive unit tests.

# slides: /assets/presentations/...
6 changes: 6 additions & 0 deletions _includes/header.html
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</li>
<li class="nav-item">
<a class="nav-link" href="/blog">Blog</a>
</li>
<li class="nav-item dropdown">
<a href="#" class="nav-link dropdown-toggle" id="dropdown03" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="false">CompilerResearchCon</a>
<ul class="dropdown-menu" aria-labelledby="dropdown03">
<li class="dropdown-item"> <a class="nav-link" href="/crcon2025">Year 2025</a></li>
</ul>
</li>
</ul>
</div>
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2 changes: 1 addition & 1 deletion _pages/404.md
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# 404 Page Not Found

The page you were looking for was not found.
If you believe this was in error, please contact <mailto:david.lange@princeton.edu>
If you believe this was an error, please contact <mailto:david.lange@princeton.edu>

68 changes: 68 additions & 0 deletions _pages/crcon2025.md
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---
title: "CompilerResearchCon"
layout: gridlay
excerpt: "CompilerResearchCon"
sitemap: false
permalink: /crcon2025/
---

# CompilerResearchCon

Compiler Research Conferences are focused events that bring together members and
contributors to share progress and insights on specific initiatives. These
virtual gatherings provide an opportunity to present completed work, discuss
outcomes, and explore the impact of research efforts in compiler technology
and related areas. Such conferences typically feature presentations from contributors,
including participants in programs like Google Summer of Code, showcasing
developments in automatic differentiation, interpretative C/C++/CUDA, and
other compiler infrastructure projects. These events promote knowledge exchange
and celebrate the collaborative achievements of our research community.

<i>If you are interested in our work you can join our
[compiler-research-announce google groups forum](https://groups.google.com/g/compiler-research-announce)
or follow us on [LinkedIn](https://www.linkedin.com/groups/9579649/).</i>

{% assign sorted_crcon = site.data.crconlist2025 | sort: "date" | reverse %}

{% for crcon in sorted_crcon %}

<div class="row">
<span id="{{crcon.label}}">&nbsp;</span>
<div class="clearfix">
<div class="well" style="padding-left: 20px; padding-right: 20px">
<a style="text-decoration:none;" href="#{{crcon.label}}">
{{ crcon.name }} -- {{ crcon.date | date_to_long_string }} at {{crcon.time_cest}} Geneva (CH) Time
</a>
<div>Connection information: {{crcon.connect}} <br />
</div><div>
Agenda:
<ul>{% for item in crcon.agenda %}
<li><strong>{{item.time_cest}}
{% if item.speaker %}
{% if item.speaker.first %}
{{ item.speaker.name }}
<br>“{{item.title}}”</strong>
{% else %}
({{item.speaker}})
{% endif %}
{% endif %}
{% if item.description %}
<br /> <i>Abstract:</i>{{item.description | markdownify }}
{% endif %}
{% if item.slides %}
<a style="text-decoration:none;" href="{{item.slides}}">Slides</a>
{% endif %}
{% if item.video %}
<a style="text-decoration:none;" href="{{item.video}}">Video</a>
{% endif %}
{{ item.link }}
</li>
{% endfor %}</ul>
</div>
</div>
</div>

</div>

{% endfor %}

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