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Detecting Type 2 Diabetes (T2D) and Prediabetes (PD) is a real challenge for medicine due to the absence of pathogenic symptoms and the lack of known associated risk factors. Even though some proposals for machine learning models enable the identification of people at risk, the nature of the condition makes it so that a model suitable for one population may not necessarily be suitable for another. In this article, the development and assessment of predictive models to identify people at risk for T2D and PD specifically in Argentina are discussed. First, the database was thoroughly preprocessed and three specific datasets were generated considering a compromise between the number of records and the amount of available variables. After applying 5 different classification models, the results obtained show that a very good performance was observed for two datasets with some of these models. In particular, RF, DT, and ANN demonstrated great classification power, with good values for the metrics under consideration. Given the lack of this type of tool in Argentina, this work represents the first step towards the development of more sophisticated models., Comment: Computer Science - CACIC 2023 (Revised selected papers)

Graphs have become a key tool when modeling and solving problems in different areas. The Floyd-Warshall (FW) algorithm computes the shortest path between all pairs of vertices in a graph and is employed in areas like communication networking, traffic routing, bioinformatics, among others. However, FW is computationally and spatially expensive since it requires O(n^3) operations and O(n^2) memory space. As the graph gets larger, parallel computing becomes necessary to provide a solution in an acceptable time range. In this paper, we studied a FW code developed for Xeon Phi KNL processors and adapted it to run on any Intel x86 processors, losing the specificity of the former. To do so, we verified one by one the optimizations proposed by the original code, making adjustments to the base code where necessary, and analyzing its performance on two Intel servers under different test scenarios. In addition, a new optimization was proposed to increase the concurrency degree of the parallel algorithm, which was implemented using two different synchronization mechanisms. The experimental results show that all optimizations were beneficial on the two x86 platforms selected. Last, the new optimization proposal improved performance by up to 23%., Comment: Computer Science - CACIC 2023 (Revised selected papers)

Nowadays, improving the energy efficiency of high-performance computing (HPC) systems is one of the main drivers in scientific and technological research. As large-scale HPC systems require some fault-tolerant method, the opportunities to reduce energy consumption should be explored. In particular, rollback-recovery methods using uncoordinated checkpoints prevent all processes from re-executing when a failure occurs. In this context, it is possible to take actions to reduce the energy consumption of the nodes whose processes do not re-execute. This work is an extension of a previous one, in which we proposed a series of strategies to manage energy consumption at failure-time. In this work, we have enriched our simulator and the experimentation by including non-blocking communications (with and without system buffering) and a largest number of candidate processes to be analyzed. We have called the latter as \textit{cascade analysis}, because it includes processes that gets blocked by communication indirectly with the failed process. The simulations show that the savings were negligible in the worst case, but in some scenarios, it was possible to achieve significant ones; the maximum saving achieved was 90\% in a time interval of 16 minutes. As a result, we show the feasibility of improving energy efficiency in HPC systems in the presence of a failure., Comment: This is the accepted version of the manuscript that was sent to review to Journal of Parallel and Distributed Computing (ISSN 1096-0848). arXiv admin note: text overlap with arXiv:2012.11396

The heterogeneous computing paradigm has led to the need for portable and efficient programming solutions that can leverage the capabilities of various hardware devices, such as NVIDIA, Intel, and AMD GPUs. This study evaluates the portability and performance of the SYCL and CUDA languages for one fundamental bioinformatics application (Smith-Waterman protein database search) across different GPU architectures, considering single and multi-GPU configurations from different vendors. The experimental work showed that, while both CUDA and SYCL versions achieve similar performance on NVIDIA devices, the latter demonstrated remarkable code portability to other GPU architectures, such as AMD and Intel. Furthermore, the architectural efficiency rates achieved on these devices were superior in 3 of the 4 cases tested. This brief study highlights the potential of SYCL as a viable solution for achieving both performance and portability in the heterogeneous computing ecosystem., Comment: This article was accepted for publication in 2023 IEEE 35th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

Bioinformatics and Computational Biology are two fields that have been exploiting GPUs for more than two decades, being CUDA the most used programming language for them. However, as CUDA is an NVIDIA proprietary language, it implies a strong portability restriction to a wide range of heterogeneous architectures, like AMD or Intel GPUs. To face this issue, the Khronos Group has recently proposed the SYCL standard, which is an open, royalty-free, cross-platform abstraction layer, that enables the programming of a heterogeneous system to be written using standard, single-source C++ code. Over the past few years, several implementations of this SYCL standard have emerged, being oneAPI the one from Intel. This paper presents the migration process of the SW\# suite, a biological sequence alignment tool developed in CUDA, to SYCL using Intel's oneAPI ecosystem. The experimental results show that SW\# was completely migrated with a small programmer intervention in terms of hand-coding. In addition, it was possible to port the migrated code between different architectures (considering multiple vendor GPUs and also CPUs), with no noticeable performance degradation on 5 different NVIDIA GPUs. Moreover, performance remained stable when switching to another SYCL implementation. As a consequence, SYCL and its implementations can offer attractive opportunities for the Bioinformatics community, especially considering the vast existence of CUDA-based legacy codes.

To face the programming challenges related to heterogeneous computing, Intel recently introduced oneAPI, a new programming environment that allows code developed in Data Parallel C++ (DPC++) language to be run on different devices such as CPUs, GPUs, FPGAs, among others. To tackle CUDA-based legacy codes, oneAPI provides a compatibility tool (dpct) that facilitates the migration to DPC++. Due to the large amount of existing CUDA-based software in the bioinformatics context, this paper presents our experiences porting SW#db, a well-known sequence alignment tool, to DPC++ using dpct. From the experimental work, it was possible to prove the usefulness of dpct for SW#db code migration and the cross-GPU vendor, cross-architecture portability of the migrated DPC++ code. In addition, the performance results showed that the migrated DPC++ code reports similar efficiency rates to its CUDA-native counterpart or even better in some tests (approximately +5%)., Comment: Accepted in IWBBIO 2022

Currently, Python is one of the most widely used languages in various application areas. However, it has limitations when it comes to optimizing and parallelizing applications due to the nature of its official CPython interpreter, especially for CPU-bound applications. To solve this problem, several alternative translators have emerged, each with a different approach and its own cost-performance ratio. Due to the absence of comparative studies, we have carried out a performance comparison of these translators using N-Body as a case study (a well-known problem with high computational demand). The results obtained show that CPython and PyPy presented poor performance due to their limitations when it comes to parallelizing algorithms; while Numba and Cython achieved significantly higher performance, proving to be viable options to speed up numerical algorithms., Comment: In: Pesado, P., Gil, G. (eds). Computer Science - CACIC 2021. Springer Communications in Computer and Information Science, vol 1584

Historically, Fortran and C have been the default programming languages in High-Performance Computing (HPC). In both, programmers have primitives and functions available that allow manipulating system memory and interacting directly with the underlying hardware, resulting in efficient code in both response times and resource use. On the other hand, it is a real challenge to generate code that is maintainable and scalable over time in these types of languages. In 2010, Rust emerged as a new programming language designed for concurrent and secure applications, which adopts features of procedural, object-oriented and functional languages. Among its design principles, Rust is aimed at matching C in terms of efficiency, but with increased code security and productivity. This paper presents a comparative study between C and Rust in terms of performance and programming effort, selecting as a case study the simulation of N computational bodies (N-Body), a popular problem in the HPC community. Based on the experimental work, it was possible to establish that Rust is a language that reduces programming effort while maintaining acceptable performance levels, meaning that it is a possible alternative to C for HPC., Comment: This article was accepted for publication in 2021 XLVI Latin American Computing Conference (CLEI)

The heterogeneous computing paradigm represents a real programming challenge due to the proliferation of devices with different hardware characteristics. Recently Intel introduced oneAPI, a new programming environment that allows code developed in DPC++ to be run on different devices such as CPUs, GPUs, FPGAs, among others. This paper presents our first experiences in porting two CUDA applications to DPC++ using the oneAPI dpct tool. From the experimental work, it was possible to verify that dpct does not achieve 100% of the migration task; however, it performs most of the work, reporting the programmer of possible pending adaptations. Additionally, it was possible to verify the functional portability of the DPC++ code obtained, having successfully executed it on different CPU and GPU architectures., Comment: Accepted for publication in 9th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2021, https://jcc.info.unlp.edu.ar/en/)

Today, one of the main challenges for high-performance computing systems is to improve their performance by keeping energy consumption at acceptable levels. In this context, a consolidated strategy consists of using accelerators such as GPUs or many-core Intel Xeon Phi processors. In this work, devices of the NVIDIA Pascal and Intel Xeon Phi Knights Landing architectures are described and compared. Selecting the Floyd-Warshall algorithm as a representative case of graph and memory-bound applications, optimized implementations were developed to analyze and compare performance and energy efficiency on both devices. As it was expected, Xeon Phi showed superior when considering double-precision data. However, contrary to what was considered in our preliminary analysis, it was found that the performance and energy efficiency of both devices were comparable using single-precision datatype., Comment: Computer Science - CACIC 2020. CACIC 2020. Communications in Computer and Information Science, vol 1409. Springer, Cham

High-performance computing continues to increase its computing power and energy efficiency. However, energy consumption continues to rise and finding ways to limit and/or decrease it is a crucial point in current research. For high-performance MPI applications, there are rollback recovery based fault tolerance methods, such as uncoordinated checkpoints. These methods allow only some processes to go back in the face of failure, while the rest of the processes continue to run. In this article, we focus on the processes that continue execution, and propose a series of strategies to manage energy consumption when a failure occurs and uncoordinated checkpoints are used. We present an energy model to evaluate strategies and through simulation we analyze the behavior of an application under different configurations and failure time. As a result, we show the feasibility of improving energy efficiency in HPC systems in the presence of a failure., Comment: This is the author version of the manuscript that was accepted for publication in 2020 IEEE Biennial Congress of Argentina (ARGENCON) (ISBN 978-1-7281-5957-7/20)

The increasing complexity in today's systems and the limited market times demand new development tools for FPGA. Currently, in addition to traditional hardware description languages (HDLs), there are high-level synthesis (HLS) tools that increase the abstraction level in system development. Despite the greater simplicity of design and testing, HLS has some drawbacks in describing harware. This paper presents a comparative study between HLS and HDL for FPGA, using a Sobel filter as a case study in the image processing field. The results show that the HDL implementation is slightly better than the HLS version considering resource usage and response time. However, the programming effort required in the HDL solution is significantly larger than in the HLS counterpart.

Diabetes Mellitus (DM) is a chronic disease characterized by an increase in blood glucose (sugar) above normal levels and it appears when human body is not able to produce enough insulin to cover the peripheral tissue demand. Nowadays, DM affects the 8.5% of the world's population and, even though no cure for it has been found, an adequate monitoring and treatment allow patients to have an almost normal life. This paper introduces Diabetes Link, a comprehensive platform for control and monitoring people with DM. Diabetes Link allows recording various parameters relevant for the treatment and calculating different statistical charts using them. In addition, it allows connecting with other users (supervisors) so they can monitor the controls. Even more, the extensive comparative study carried out reflects that Diabetes Link presents distinctive and superior features against other proposals. We conclude that Diabetes Link represents a broad and accessible tool that can help make day-to-day control easier and optimize the efficacy in DM control and treatment., Comment: This article was accepted for publicatin on Third International Conference on Applied Informatics - ICAI 2020 (icai.itiud.org)

Handling faults is a growing concern in HPC. In future exascale systems, it is projected that silent undetected errors will occur several times a day, increasing the occurrence of corrupted results. In this article, we propose SEDAR, which is a methodology that improves system reliability against transient faults when running parallel message-passing applications. Our approach, based on process replication for detection, combined with different levels of checkpointing for automatic recovery, has the goal of helping users of scientific applications to obtain executions with correct results. SEDAR is structured in three levels: (1) only detection and safe-stop with notification; (2) recovery based on multiple system-level checkpoints; and (3) recovery based on a single valid user-level checkpoint. As each of these variants supplies a particular coverage but involves limitations and implementation costs, SEDAR can be adapted to the needs of the system. In this work, a description of the methodology is presented and the temporal behavior of employing each SEDAR strategy is mathematically described, both in the absence and presence of faults. A model that considers all the fault scenarios on a test application is introduced to show the validity of the detection and recovery mechanisms. An overhead evaluation of each variant is performed with applications involving different communication patterns; this is also used to extract guidelines about when it is beneficial to employ each SEDAR protection level. As a result, we show its efficacy and viability to tolerate transient faults in target HPC environments., Comment: 26 pages, 3 figures (1 and 2 with subfigures), 4 tables, sent to review to Future Generation Computer Systems

Manycores are consolidating in HPC community as a way of improving performance while keeping power efficiency. Knights Landing is the recently released second generation of Intel Xeon Phi architecture. While optimizing applications on CPUs, GPUs and first Xeon Phi's has been largely studied in the last years, the new features in Knights Landing processors require the revision of programming and optimization techniques for these devices. In this work, we selected the Floyd-Warshall algorithm as a representative case study of graph and memory-bound applications. Starting from the default serial version, we show how data, thread and compiler level optimizations help the parallel implementation to reach 338 GFLOPS., Comment: Computer Science - CACIC 2017. Springer Communications in Computer and Information Science, vol 790

The well-known Smith-Waterman (SW) algorithm is the most commonly used method for local sequence alignments. However, SW is very computationally demanding for large protein databases. There exist several implementations that take advantage of computing parallelization on many-cores, FPGAs or GPUs, in order to increase the alignment throughtput. In this paper, we have explored SW acceleration on Intel KNL processor. The novelty of this architecture requires the revision of previous programming and optimization techniques on many-core architectures. To the best of authors knowledge, this is the first KNL architecture assessment for SW algorithm. Our evaluation, using the renowned Environmental NR database as benchmark, has shown that multi-threading and SIMD exploitation reports competitive performance (351 GCUPS) in comparison with other implementations., Comment: Submitted to Euro-Par 2017 Conference