arXiv subjects

Manasvi Goyal

Publications and source records attributed to Manasvi Goyal.

4 recordsLinked to original sources

arXiv2026

Advancing Awkward Arrays for High-Performance CPU and GPU Processing

Awkward Array is a Python library for representing and processing nested, variable-length data that is widely used in high-energy physics. As HL-LHC analyses increasingly rely on accelerator hardware, efficient execution of irregular workloads has become essential. While dense numerical arrays map naturally to GPUs, nested and variable-length data structures remain significantly more difficult to accelerate because they require indirect indexing, segmented operations, and irregular memory access patterns. We present recent developments in the Awkward Array GPU backend, including CUDA implementations built on NVIDIA CUDA Core Compute Libraries (CCCL), optimized memory management, and segmented reduction algorithms for ragged arrays. These developments preserve the existing Python programming model while substantially improving GPU throughput on irregular workloads. We describe the backend architecture, automated validation framework, and benchmark results comparing CPU, CuPy, and CUDA implementations.

cs.SE↗

arXiv2024

Image Classification Using Singular Value Decomposition and Optimization

This study investigates the applicability of Singular Value Decomposition for the image classification of specific breeds of cats and dogs using fur color as the primary identifying feature. Sequential Quadratic Programming (SQP) is employed to construct optimally weighted templates. The proposed method achieves 69% accuracy using the Frobenius norm at rank 10. The results partially validate the assumption that dominant features, such as fur color, can be effectively captured through low-rank approximations. However, the accuracy suggests that additional features or methods may be required for more robust classification, highlighting the trade-off between simplicity and performance in resource-constrained environments.

cs.CV↗

arXiv2024

Describe Data to get Science-Data-Ready Tooling: Awkward as a Target for Kaitai Struct YAML

In some fields, scientific data formats differ across experiments due to specialized hardware and data acquisition systems. Researchers need to develop, document, and maintain experiment-specific analysis software to interact with these data formats. These software are often tightly coupled with a particular data format. This proliferation of custom data formats has been a prominent challenge for small to mid-scale experiments. The widespread adoption of ROOT has largely mitigated this problem for the Large Hadron Collider experiments. However, many smaller experiments continue to use custom data formats to meet specific research needs. Therefore, simplifying the process of accessing a unique data format for analysis holds immense value for scientific communities within HEP. We have added Awkward Arrays as a target language for Kaitai Struct for this purpose. Researchers can describe their custom data format in the Kaitai Struct YAML (KSY) language. The Kaitai Struct Compiler generates C++ code to fill the LayoutBuilder buffers using the KSY format. In a few steps, the Kaitai Struct Awkward Runtime API can convert the generated C++ code into a compiled Python module. Finally, the raw data can be passed to the module to produce Awkward Arrays. This paper introduces the Awkward Target for the Kaitai Struct Compiler and the Kaitai Struct Awkward Runtime API. It also demonstrates the conversion of a given KSY for a specific custom file format to Awkward Arrays.

cs.PL↗

arXiv2023

The Awkward World of Python and C++

There are undeniable benefits of binding Python and C++ to take advantage of the best features of both languages. This is especially relevant to the HEP and other scientific communities that have invested heavily in the C++ frameworks and are rapidly moving their data analyses to Python. Version 2 of Awkward Array, a Scikit-HEP Python library, introduces a set of header-only C++ libraries that do not depend on any application binary interface. Users can directly include these libraries in their compilation instead of linking against platform-specific libraries. This new development makes the integration of Awkward Arrays into other projects easier and more portable, as the implementation is easily separable from the rest of the Awkward Array codebase. The code is minimal; it does not include all of the code needed to use Awkward Arrays in Python, nor does it include references to Python or pybind11. The C++ users can use it to make arrays and then copy them to Python without any specialized data types - only raw buffers, strings, and integers. This C++ code also simplifies the process of just-in-time (JIT) compilation in ROOT. This implementation approach solves some of the drawbacks, like packaging projects where native dependencies can be challenging. In this paper, we demonstrate the technique to integrate C++ and Python using a header-only approach. We also describe the implementation of a new LayoutBuilder and a GrowableBuffer. Furthermore, examples of wrapping the C++ data into Awkward Arrays and exposing Awkward Arrays to C++ without copying them are discussed.

cs.MS↗