arXiv subjects

Felipe A. Cruz

Publications and source records attributed to Felipe A. Cruz.

7 recordsLinked to original sources

arXiv2026

Sarus Suite: Cloud-native Containers for HPC

High-performance computing (HPC) systems must support fast-moving software stacks, especially in AI/ML, while preserving scheduler control, scalable startup, and production performance. Yet many HPC container solutions rely on specialized runtime stacks that weaken continuity with mainstream cloud-native workflows and require ongoing effort to sustain compatibility with the evolving upstream ecosystem. We argue that HPC should specialize the integration layer while keeping the container engine aligned with upstream container evolution. We present Sarus Suite, an upstream-aligned HPC container architecture built around an unchanged Podman engine. Sarus Suite adds the HPC-specific functionality needed for production use through complementary system layers for declarative runtime specification, scheduler-native execution, scalable shared-image access, and standards-based host capability injection. We evaluate Sarus Suite on a Cray EX GH200 system using communication-intensive HPC workloads, large scale AI training, metadata-heavy startup workloads, and container startup measurements. Across PyFR, SPH-EXA, Megatron-LM, and Pynamic, Sarus Suite matches the performance and scaling of the production Enroot+Pyxis baseline while delivering consistently faster per-node container startup. The architecture also enables direct use of upstream OCI images, including NGC-based images, and supports cloud-native multi-container workflows expressed through Kubernetes manifests. These results show that HPC-grade containers do not require an HPC-specific runtime, provided that scheduler semantics, scalable image access, and host integration are implemented in explicit system layers. This preserves upstream continuity and software agility while maintaining scheduler control, scalability, and production performance.

cs.DC↗

arXiv2019

FirecREST: RESTful API on Cray XC systems

As science gateways are becoming an increasingly popular digital interface for scientific communities, it is also becoming increasingly important for High-Performance Computing centers to provide a modern Web-enabled APIs. With such interface, science gateways can easily integrate access to HPC center resources. This work presents the FirecREST API, a RESTful Web API infrastructure that allows scientific communities to access the various integrated resources and services available from the Cray XC systems at the Swiss National Supercomputing Centre. FirecREST requirements are derived from use cases described in this work.

cs.DC↗

arXiv2017

Portable, high-performance containers for HPC

Building and deploying software on high-end computing systems is a challenging task. High performance applications have to reliably run across multiple platforms and environments, and make use of site-specific resources while resolving complicated software-stack dependencies. Containers are a type of lightweight virtualization technology that attempt to solve this problem by packaging applications and their environments into standard units of software that are: portable, easy to build and deploy, have a small footprint, and low runtime overhead. In this work we present an extension to the container runtime of Shifter that provides containerized applications with a mechanism to access GPU accelerators and specialized networking from the host system, effectively enabling performance portability of containers across HPC resources. The presented extension makes possible to rapidly deploy high-performance software on supercomputers from containerized applications that have been developed, built, and tested in non-HPC commodity hardware, e.g. the laptop or workstation of a researcher.

cs.DC↗

arXiv2014

A GRASS GIS parallel module for radio-propagation predictions

Geographical information systems are ideal candidates for the application of parallel programming techniques, mainly because they usually handle large data sets. To help us deal with complex calculations over such data sets, we investigated the performance constraints of a classic master-worker parallel paradigm over a message-passing communication model. To this end, we present a new approach that employs an external database in order to improve the calculation/communication overlap, thus reducing the idle times for the worker processes. The presented approach is implemented as part of a parallel radio-coverage prediction tool for the GRASS environment. The prediction calculation employs digital elevation models and land-usage data in order to analyze the radio coverage of a geographical area. We provide an extended analysis of the experimental results, which are based on real data from an LTE network currently deployed in Slovenia. Based on the results of the experiments, which were performed on a computer cluster, the new approach exhibits better scalability than the traditional master-worker approach. We successfully tackled real-world data sets, while greatly reducing the processing time and saturating the hardware utilization.

cs.DC↗

arXiv2011

How to obtain efficient GPU kernels: an illustration using FMM & FGT algorithms

Computing on graphics processors is maybe one of the most important developments in computational science to happen in decades. Not since the arrival of the Beowulf cluster, which combined open source software with commodity hardware to truly democratize high-performance computing, has the community been so electrified. Like then, the opportunity comes with challenges. The formulation of scientific algorithms to take advantage of the performance offered by the new architecture requires rethinking core methods. Here, we have tackled fast summation algorithms (fast multipole method and fast Gauss transform), and applied algorithmic redesign for attaining performance on gpus. The progression of performance improvements attained illustrates the exercise of formulating algorithms for the massively parallel architecture of the gpu. The end result has been gpu kernels that run at over 500 Gigaflops on one nvidia Tesla C1060 card, thereby reaching close to practical peak. We can confidently say that gpu computing is not just a vogue, it is truly an irresistible trend in high-performance computing.

cs.MS↗

arXiv2009

PetFMM--A dynamically load-balancing parallel fast multipole library

Fast algorithms for the computation of $N$-body problems can be broadly classified into mesh-based interpolation methods, and hierarchical or multiresolution methods. To this last class belongs the well-known fast multipole method (FMM), which offers O(N) complexity. This paper presents an extensible parallel library for $N$-body interactions utilizing the FMM algorithm, built on the framework of PETSc. A prominent feature of this library is that it is designed to be extensible, with a view to unifying efforts involving many algorithms based on the same principles as the FMM and enabling easy development of scientific application codes. The paper also details an exhaustive model for the computation of tree-based $N$-body algorithms in parallel, including both work estimates and communications estimates. With this model, we are able to implement a method to provide automatic, a priori load balancing of the parallel execution, achieving optimal distribution of the computational work among processors and minimal inter-processor communications. Using a client application that performs the calculation of velocity induced by $N$ vortex particles, ample verification and testing of the library was performed. Strong scaling results are presented with close to a million particles in up to 64 processors, including both speedup and parallel efficiency. The library is currently able to achieve over 85% parallel efficiency for 64 processors. The software library is open source under the PETSc license; this guarantees the maximum impact to the scientific community and encourages peer-based collaboration for the extensions and applications.

cs.DC↗

arXiv2008

Characterization of the errors of the FMM in particle simulations

The Fast Multipole Method (FMM) offers an acceleration for pairwise interaction calculation, known as $N$-body problems, from $\mathcal{O}(N^2)$ to $\mathcal{O}(N)$ with $N$ particles. This has brought dramatic increase in the capability of particle simulations in many application areas, such as electrostatics, particle formulations of fluid mechanics, and others. Although the literature on the subject provides theoretical error bounds for the FMM approximation, there are not many reports of the measured errors in a suite of computational experiments. We have performed such an experimental investigation, and summarized the results of about 1000 calculations using the FMM algorithm, to characterize the accuracy of the method in relation with the different parameters available to the user. In addition to the more standard diagnostic of the maximum error, we supply illustrations of the spatial distribution of the errors, which offers visual evidence of all the contributing factors to the overall approximation accuracy: multipole expansion, local expansion, hierarchical spatial decomposition (interaction lists, local domain, far domain). This presentation is a contribution to any researcher wishing to incorporate the FMM acceleration to their application code, as it aids in understanding where accuracy is gained or compromised.

cs.DS↗