Browsing by Author "Álvarez Martí, Lluc"
Now showing items 21-28 of 28
-
Runtime-guided management of scratchpad memories in multicore architectures
Álvarez Martí, Lluc; Moretó Planas, Miquel; Casas, Marc; Castillo Villar, Emilio; Martorell Bofill, Xavier; Labarta Mancho, Jesús José; Ayguadé Parra, Eduard; Valero Cortés, Mateo (Institute of Electrical and Electronics Engineers (IEEE), 2015)
Conference report
Open AccessThe increasing number of cores and the anticipated level of heterogeneity in upcoming multicore architectures cause important problems in traditional cache hierarchies. A good way to alleviate these problems is to add ... -
Runtime-guided management of stacked DRAM memories in task parallel programs
Álvarez Martí, Lluc; Casas, Marc; Labarta Mancho, Jesús José; Ayguadé Parra, Eduard; Valero Cortés, Mateo; Moretó Planas, Miquel (Association for Computing Machinery (ACM), 2018)
Conference report
Open AccessStacked DRAM memories have become a reality in High-Performance Computing (HPC) architectures. These memories provide much higher bandwidth while consuming less power than traditional off-chip memories, but their limited ... -
TD-NUCA: runtime driven management of NUCA caches in task dataflow programming models
Caheny, Paul; Álvarez Martí, Lluc; Casas, Marc; Moretó Planas, Miquel (Institute of Electrical and Electronics Engineers (IEEE), 2022)
Conference report
Open AccessIn high performance processors, the design of on-chip memory hierarchies is crucial for performance and energy efficiency. Current processors rely on large shared Non-Uniform Cache Architectures (NUCA) to improve performance ... -
Teaching HPC systems and parallel programming with small-scale clusters
Álvarez Martí, Lluc; Ayguadé Parra, Eduard; Mantovani, Filippo (Institute of Electrical and Electronics Engineers (IEEE), 2019)
Conference report
Open AccessIn the last decades, the continuous proliferation of High-Performance Computing (HPC) systems and data centers has augmented the demand for expert HPC system designers, administrators, and programmers. For this reason, ... -
The DeepHealth Toolkit: A unified framework to boost biomedical applications
Cancilla, Michele; Canalini, Laura; Bolelli, Federico; Allegretti, Stefano; Carrión Ponz, Salvador; Paredes Palacios, Roberto; Gómez Adrián, Jon A.; Leo, Simone; Piras, Marco Enrico; Pireddu, Luca; Badouh, Asaf; Marco-Sola, Santiago; Álvarez Martí, Lluc; Moretó Planas, Miquel; Grana, Costantino (Institute of Electrical and Electronics Engineers (IEEE), 2021)
Conference report
Open AccessGiven the overwhelming impact of machine learning on the last decade, several libraries and frameworks have been developed in recent years to simplify the design and training of neural networks, providing array-based ... -
Transparent management of scratchpad memories in shared memory programming models
Álvarez Martí, Lluc (Universitat Politècnica de Catalunya, 2015-12-16)
Doctoral thesis
Open AccessCache-coherent shared memory has traditionally been the favorite memory organization for chip multiprocessors thanks to its high programmability. In this organization the cache hierarchy is in charge of moving the data and ... -
WFA-FPGA: An efficient accelerator of the wavefront algorithm for short and long read genomics alignment
Haghi, Abbas; Marco-Sola, Santiago; Álvarez Martí, Lluc; Diamantopoulos, Dionysios; Hagleitner, Christoph; Moretó Planas, Miquel (Elsevier, 2023-12)
Article
Open AccessIn the last years, advances in genome sequencing technologies have enabled the proliferation of genomic applications that guide personalized medicine. These applications have an enormous computational cost due to the large ... -
WFAsic: A high-performance ASIC accelerator for DNA sequence alignment on a RISC-V SoC
Haghi, Abbas; Álvarez Martí, Lluc; Fornt Mas, Jordi; Haro Ruiz, Juan Miguel de; Figueras Bagué, Roger; Doblas Font, Max; Marco Sola, Santiago; Moretó Planas, Miquel (Association for Computing Machinery (ACM), 2023)
Conference report
Open AccessThe ever-increasing yields in genome sequence data production pose a computational challenge to current genome sequence analysis tools, jeopardizing the future of personalized medicine. Leveraging hardware accelerators ...