{"id":3801,"date":"2023-01-24T10:20:58","date_gmt":"2023-01-24T08:20:58","guid":{"rendered":"https:\/\/gaz-temporal.i3a.es\/?p=3801"},"modified":"2023-01-24T10:22:06","modified_gmt":"2023-01-24T08:22:06","slug":"pablo-ibanez","status":"publish","type":"post","link":"https:\/\/gaz.i3a.es\/es\/pablo-ibanez\/","title":{"rendered":"Pablo Iba\u00f1ez"},"content":{"rendered":"
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    Investigadores<\/a><\/h3>\n

    Pablo Iba\u00f1ez<\/strong><\/h1>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/li>\n\t\t<\/ul>\t\t\t\t
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      Senior Lecturer<\/strong><\/p>\n

      Email:<\/strong> imarin@unizar.es<\/a><\/p>\n

      Address:<\/strong> Campus R\u00edo Ebro, University of Zaragoza
      \nC\/Mar\u00eda de Luna 1, Ada Byron Building,
      \n50018, Zaragoza, Spain<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n\n

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      \n\t\t\t\t\t\tABOUT ME\t\t\t\t\t\t<\/h5>\n\t\t\t\t\t\t\t\t\t\t\t
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      —<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>

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      \n\t\t\t\t\t\tPUBLICATIONS\t\t\t\t\t\t<\/h5>\n\t\t\t\t\t\t\t\t\t\t\t
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      <\/a>
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      85 registros<\/span> «<\/a> ‹<\/a> 1 de 17 ›<\/a> »<\/a> <\/div><\/div>

      2025<\/h3>

      Art\u00edculos de revista<\/h3>

      Navarro-Torres, Agust\u00edn; Panda, Biswabandan; Alastruey-Bened\u00e9, Jes\u00fas; Ib\u00e1\u00f1ez, Pablo; Vi\u00f1nals-Y\u00fafera, V\u00edctor; Ros, Alberto<\/p>

      A Complexity-Effective Local Delta Prefetcher Art\u00edculo de revista<\/span> <\/p>

      En: <\/span>IEEE Transactions on Computers, <\/span>2025<\/span>.<\/p>

      BibTeX<\/a><\/span><\/p>

      @article{navarro2025complexity,
      \r\ntitle = {A Complexity-Effective Local Delta Prefetcher},
      \r\nauthor = {Agust\u00edn Navarro-Torres and Biswabandan Panda and Jes\u00fas Alastruey-Bened\u00e9 and Pablo Ib\u00e1\u00f1ez and V\u00edctor Vi\u00f1nals-Y\u00fafera and Alberto Ros},
      \r\nyear  = {2025},
      \r\ndate = {2025-01-01},
      \r\njournal = {IEEE Transactions on Computers},
      \r\npublisher = {IEEE},
      \r\nkeywords = {},
      \r\npubstate = {published},
      \r\ntppubtype = {article}
      \r\n}
      \r\n<\/pre><\/div>
      Cerrar<\/a><\/p><\/div><\/div><\/div>
       L\u00f3pez-Villellas, Lori\u00e9n;  Mikkelsen, Carl Christian Kjelgaard;  Galano-Frutos, Juan Jos\u00e9;  Marco-Sola, Santiago;  Alastruey-Bened\u00e9, Jes\u00fas;  Ib\u00e1\u00f1ez, Pablo;  Echenique, Pablo;  Moret\u00f3, Miquel;  Rosa, Maria Cristina De;  Garc\u00eda-Risue\u00f1o, Pablo<\/p>
      ILVES: Accurate and Efficient Bond Length and Angle Constraints in Molecular Dynamics Art\u00edculo de revista<\/span> <\/p>
      En: <\/span>Journal of Chemical Theory and Computation, <\/span>vol. 21, <\/span>no 18, <\/span>pp. 8711\u20138719, <\/span>2025<\/span>.<\/p>
      BibTeX<\/a><\/span><\/p>
      @article{lopez2025ilves,
      \r\ntitle = {ILVES: Accurate and Efficient Bond Length and Angle Constraints in Molecular Dynamics},
      \r\nauthor = {Lori\u00e9n L\u00f3pez-Villellas and Carl Christian Kjelgaard Mikkelsen and Juan Jos\u00e9 Galano-Frutos and Santiago Marco-Sola and Jes\u00fas Alastruey-Bened\u00e9 and Pablo Ib\u00e1\u00f1ez and Pablo Echenique and Miquel Moret\u00f3 and Maria Cristina De Rosa and Pablo Garc\u00eda-Risue\u00f1o},
      \r\nyear  = {2025},
      \r\ndate = {2025-01-01},
      \r\njournal = {Journal of Chemical Theory and Computation},
      \r\nvolume = {21},
      \r\nnumber = {18},
      \r\npages = {8711\u20138719},
      \r\npublisher = {American Chemical Society},
      \r\nkeywords = {},
      \r\npubstate = {published},
      \r\ntppubtype = {article}
      \r\n}
      \r\n<\/pre><\/div>
      Cerrar<\/a><\/p><\/div><\/div><\/div>
       L\u00f3pez-Villellas, Lori\u00e9n;  I\u00f1iguez, Cristian;  Jim\u00e9nez-Blanco, Albert;  Aguado-Puig, Quim;  Moret\u00f3, Miquel;  Alastruey-Bened\u00e9, Jes\u00fas;  Ib\u00e1\u00f1ez, Pablo;  Marco-Sola, Santiago<\/p>
      Singletrack: An Algorithm for Improving Memory Consumption and Performance of Gap-Affine Sequence Alignment Art\u00edculo de revista<\/span> <\/p>
      En: <\/span>bioRxiv, <\/span>pp. 2025\u201310, <\/span>2025<\/span>.<\/p>
      BibTeX<\/a><\/span><\/p>
      @article{lopez2025singletrack,
      \r\ntitle = {Singletrack: An Algorithm for Improving Memory Consumption and Performance of Gap-Affine Sequence Alignment},
      \r\nauthor = {Lori\u00e9n L\u00f3pez-Villellas and Cristian I\u00f1iguez and Albert Jim\u00e9nez-Blanco and Quim Aguado-Puig and Miquel Moret\u00f3 and Jes\u00fas Alastruey-Bened\u00e9 and Pablo Ib\u00e1\u00f1ez and Santiago Marco-Sola},
      \r\nyear  = {2025},
      \r\ndate = {2025-01-01},
      \r\njournal = {bioRxiv},
      \r\npages = {2025\u201310},
      \r\npublisher = {Cold Spring Harbor Laboratory},
      \r\nkeywords = {},
      \r\npubstate = {published},
      \r\ntppubtype = {article}
      \r\n}
      \r\n<\/pre><\/div>
      Cerrar<\/a><\/p><\/div><\/div><\/div>
      2024<\/h3>
      Art\u00edculos de revista<\/h3>
       L\u00f3pez-Villellas, Lori\u00e9n;  Langarita-Ben\u00edtez, Rub\u00e9n;  Badouh, Asaf;  Soria-Pardos, V\u00edctor;  Aguado-Puig, Quim;  L\u00f3pez-Parad\u00eds, Guillem;  Doblas, Max;  Setoain, Javier;  Kim, Chulho;  Ono, Makoto;  Armejach, Adri\u00e0;  Marco-Sola, Santiago;  Alastruey-Bened\u00e9, Jes\u00fas;  Ib\u00e1\u00f1ez, Pablo;  Moret\u00f3, Miquel<\/p>
      GenArchBench: A genomics benchmark suite for arm HPC processors<\/a> Art\u00edculo de revista<\/span> <\/p>
      En: <\/span>Future Generation Computer Systems, <\/span>vol. 157, <\/span>pp. 313-329, <\/span>2024<\/span>, ISSN: 0167-739X<\/span>.<\/p>
      Resumen<\/a><\/span> | Enlaces<\/a><\/span> | BibTeX<\/a><\/span><\/p>
      @article{LOPEZVILLELLAS2024313,
      \r\ntitle = {GenArchBench: A genomics benchmark suite for arm HPC processors},
      \r\nauthor = {Lori\u00e9n L\u00f3pez-Villellas and Rub\u00e9n Langarita-Ben\u00edtez and Asaf Badouh and V\u00edctor Soria-Pardos and Quim Aguado-Puig and Guillem L\u00f3pez-Parad\u00eds and Max Doblas and Javier Setoain and Chulho Kim and Makoto Ono and Adri\u00e0 Armejach and Santiago Marco-Sola and Jes\u00fas Alastruey-Bened\u00e9 and Pablo Ib\u00e1\u00f1ez and Miquel Moret\u00f3},
      \r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X24001250},
      \r\ndoi = {https:\/\/doi.org\/10.1016\/j.future.2024.03.050},
      \r\nissn = {0167-739X},
      \r\nyear  = {2024},
      \r\ndate = {2024-01-01},
      \r\njournal = {Future Generation Computer Systems},
      \r\nvolume = {157},
      \r\npages = {313-329},
      \r\nabstract = {Arm usage has substantially grown in the High-Performance Computing (HPC) community. Japanese supercomputer Fugaku, powered by Arm-based A64FX processors, held the top position on the Top500 list between June 2020 and June 2022, currently sitting in the fourth position. The recently released 7th generation of Amazon EC2 instances for compute-intensive workloads (C7 g) is also powered by Arm Graviton3 processors. Projects like European Mont-Blanc and U.S. DOE\/NNSA Astra are further examples of Arm irruption in HPC. In parallel, over the last decade, the rapid improvement of genomic sequencing technologies and the exponential growth of sequencing data has placed a significant bottleneck on the computational side. While most genomics applications have been thoroughly tested and optimized for x86 systems, just a few are prepared to perform efficiently on Arm machines. Moreover, these applications do not exploit the newly introduced Scalable Vector Extensions (SVE). This paper presents GenArchBench, the first genome analysis benchmark suite targeting Arm architectures. We have selected computationally demanding kernels from the most widely used tools in genome data analysis and ported them to Arm-based A64FX and Graviton3 processors. Overall, the GenArch benchmark suite comprises 13 multi-core kernels from critical stages of widely-used genome analysis pipelines, including base-calling, read mapping, variant calling, and genome assembly. Our benchmark suite includes different input data sets per kernel (small and large), each with a corresponding regression test to verify the correctness of each execution automatically. Moreover, the porting features the usage of the novel Arm SVE instructions, algorithmic and code optimizations, and the exploitation of Arm-optimized libraries. We present the optimizations implemented in each kernel and a detailed performance evaluation and comparison of their performance on four different HPC machines (i.e., A64FX, Graviton3, Intel Xeon Skylake Platinum, and AMD EPYC Rome). Overall, the experimental evaluation shows that Graviton3 outperforms other machines on average. Moreover, we observed that the performance of the A64FX is significantly constrained by its small memory hierarchy and latencies. Additionally, as proof of concept, we study the performance of a production-ready tool that exploits two of the ported and optimized genomic kernels.},
      \r\nkeywords = {},
      \r\npubstate = {published},
      \r\ntppubtype = {article}
      \r\n}
      \r\n<\/pre><\/div>
      Cerrar<\/a><\/p><\/div>
      Arm usage has substantially grown in the High-Performance Computing (HPC) community. Japanese supercomputer Fugaku, powered by Arm-based A64FX processors, held the top position on the Top500 list between June 2020 and June 2022, currently sitting in the fourth position. The recently released 7th generation of Amazon EC2 instances for compute-intensive workloads (C7 g) is also powered by Arm Graviton3 processors. Projects like European Mont-Blanc and U.S. DOE\/NNSA Astra are further examples of Arm irruption in HPC. In parallel, over the last decade, the rapid improvement of genomic sequencing technologies and the exponential growth of sequencing data has placed a significant bottleneck on the computational side. While most genomics applications have been thoroughly tested and optimized for x86 systems, just a few are prepared to perform efficiently on Arm machines. Moreover, these applications do not exploit the newly introduced Scalable Vector Extensions (SVE). This paper presents GenArchBench, the first genome analysis benchmark suite targeting Arm architectures. We have selected computationally demanding kernels from the most widely used tools in genome data analysis and ported them to Arm-based A64FX and Graviton3 processors. Overall, the GenArch benchmark suite comprises 13 multi-core kernels from critical stages of widely-used genome analysis pipelines, including base-calling, read mapping, variant calling, and genome assembly. Our benchmark suite includes different input data sets per kernel (small and large), each with a corresponding regression test to verify the correctness of each execution automatically. Moreover, the porting features the usage of the novel Arm SVE instructions, algorithmic and code optimizations, and the exploitation of Arm-optimized libraries. We present the optimizations implemented in each kernel and a detailed performance evaluation and comparison of their performance on four different HPC machines (i.e., A64FX, Graviton3, Intel Xeon Skylake Platinum, and AMD EPYC Rome). Overall, the experimental evaluation shows that Graviton3 outperforms other machines on average. Moreover, we observed that the performance of the A64FX is significantly constrained by its small memory hierarchy and latencies. Additionally, as proof of concept, we study the performance of a production-ready tool that exploits two of the ported and optimized genomic kernels.<\/div>
      Cerrar<\/a><\/p><\/div>