Marco Edoardo Santimaria

@misc{24:santimaria:bighpc,

title = {Releasing the CAPIO middleware from MPI derived constraints},

author = {Marco Edoardo Santimaria and Iacopo Colonnelli and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/zrJGD4i36fWdp5g},

year  = {2024},

date = {2024-09-01},

address = {Pisa, Italy},

abstract = {CAPIO is a middleware that transparently injects streaming capabilities into file-based workflows. However, its implementation is limited to HPC environments based on the MPI framework, significantly limiting its applications. This paper will illustrate a proposed architecture and some preliminary results aimed at investigating the usage of a distributed files system as a communication media for the CAPIO middleware, with the ultimate goal of supporting both CLOUD-based and HPC-based workflows.},

keywords = {across, admire, capio, capiocl, eupex, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:santimaria:itadata:shpcpee,

title = {CAPIO: Cross Application Programamble IO},

author = {Marco Edoardo Santimaria and Iacopo Colonnelli and Massimo Torquati and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/rg6LWwrZXi6tTXm},

year  = {2024},

date = {2024-09-01},

address = {Pisa, Italy},

abstract = {With the increasing amount of digital data available for analysis and simulation, the class of I/O-intensive HPC workflows is fated to expand, further exacerbating quickly the performance gap between computing, memory, and storage technologies. CAPIO (Cross-Application Programmable I/O), is a middleware capable of injecting I/O streaming capabilities into file-based workflows, improving the computation-I/O overlap without the need to change the application code. In this presentation, we will introduce the CAPIO-CL language with its semantics, as well as the implementation of the CAPIO-CL language through the CAPIO middleware. We will also provide some case studies of how CAPIO has been employed to improve workflow execution time as well as some future directions.},

keywords = {across, admire, capio, capiocl, eupex, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:santimaria:hlpp:capiocl,

title = {CAPIO-CL: Cross Application Programmable IO - Coordination Language},

author = {Marco Edoardo Santimaria},

url = {https://datacloud.di.unito.it/index.php/s/zsKY3PWzX5NFCiX},

year  = {2024},

date = {2024-07-01},

address = {Pisa, Italy},

abstract = {The performance bottleneck in file-based workflows remains a pressing issue in the realm of I/O-based workflows. To address this challenge, a novel annotation language has been developed. CAPIO-CL is positioned as an innovative I/O coordination language, enabling users to annotate data dependencies within file-based workflows with synchronization semantics pertinent to the involved files and directories. Through the information provided by the language, optimization opportunities arise in streaming and preemptive data movement. This paper serves to illustrate the semantics and syntax enabling CAPIO-CL to enhance the performance of in situ workflows without necessitating the rewriting or modification of the original workflow application steps. Finally, an analysis of CAPIO-CL is provided, taking into consideration both language expressiveness and application performance enhancement.},

keywords = {across, admire, capio, eupex, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:pdp:karmarkartalk,

title = {Benchmarking Parallelization Models through Karmarkar`s algorithm},

author = {Giulio Malenza and Marco Edoardo Santimaria},

url = {https://datacloud.di.unito.it/index.php/s/JjKcAJpYS7ctX9r},

year  = {2024},

date = {2024-03-01},

address = {Dublin, Irelans},

abstract = {Optimization problems are one of the main focus of scientific research. Their computational-intensive nature makes them prone to be parallelized with consistent improvements in performance. This paper sheds light on different parallel models for accelerating Karmarkar’s Interior-point method. To do so, we assess parallelization strategies for individual operations within the aforementioned Karmarkar’s algorithm using OpenMP, GPU acceleration with CUDA, and the recent Parallel Standard C++ Linear Algebra library (PSTL) executing both on GPU and CPU. Our different implementations yield interesting benchmark results that show the optimal approach for parallelizing interior point algorithms for general Linear Programming (LP) problems. In addition, we propose a more theoretical perspective of the parallelization of this algorithm, with a detailed study of our OpenMP implementation, showing the limits of optimizing the single operations},

keywords = {HPC, icsc},

pubstate = {published},

tppubtype = {misc}

}