New technologies to lower the temperature of computing systems, developed by the EuroHPC JU-funded TEXTAROSSA project, could lead to exascale supercomputers that are faster and more energy-efficient.
CORDIS interviewed the TEXTAROSSA project coordinator M. Celino: https://cordis.europa.eu/article/id/452284-helping-next-gen-supercomputers-keep-their-cool
Antonio Filgueras (BSC) presented at DSD conference 2024 the results of the TEXTAROSSA project with a presentation entitled The TEXTAROSSA Project: Cool all the Way Down to the Hardware.
The paper, that will be published in the coming months, was authored by Antonio Filgueras, Giovanni Agosta, Marco Aldinucci, Carlos Álvarez, Pasqua D’Ambra, Massimo Bernaschi, Andrea Biagioni, Daniele Cattaneo, Alessandro Celestini, Massimo Celino, Carlotta Chiarini, Francesca Lo Cicero, Paolo Cretaro, William Fornaciari, Ottorino Frezza, Francesco Giacomini, Juan Miguel de Haro Ruiz, Francesco Iannone, Daniel Jaschke, Daniel Jiménez-González, Michal Kulczewski, Alberto Leva, Alessandro Lonardo, Michele Martinelli, Xavier Martorell, Simone Montangero, Lucas Morais, Ariel Oleksiak, Paolo Palazzari, Luca Pontisso, Federico Reghenzani, Cristian Rossi, Sergio Saponara, Carlo Saverio Lodi, Francesco Simula, Federico Terraneo, Piero Vicini, Miquel Vidal and Giuseppe Zummo.
Download here the presentation.
New technologies to lower the temperature of computing systems, developed by the EuroHPC JU-funded TEXTAROSSA project, could lead to exascale supercomputers that are faster and more energy-efficient.
Supercomputers can answer some of our most complex scientific questions. This CORDIS Results Pack on supercomputing highlights 15 European projects supported by the unique European High Performance Computing Joint Undertaking (EuroHPC JU), which aims to put Europe at the forefront of the high-performance computing and quantum computing revolution. The investment made by the EuroHPC JU supports the objective of building a world-class supercomputing and quantum computing ecosystem in Europe, one that will accelerate research and industry, boost European competitiveness and innovation and improve the quality of life of European citizens.
BSC research in TEXTAROSSA focused on improving the management of parallel applications, introducing the scheduler in the hardware. Scheduling is moved into the hardware by the fast task scheduler component, which was integrated within the RISC-V processors. Specifically, the BSC-developed 30-core RISC-V system demonstrated how hardware Task Scheduling can provide competitive performance with a high number of cores and, incorporates hardware and software optimizations that make this solution even more scalable.
Read more on the participation of BSC in the TEXTAROSSA project.
An innovative two-phase cooling system has been developed within the EuroHPC Joint Undertaking (EuroHPC JU) project TEXTAROSSA. This innovative solution addresses the pressing cooling challenges faced by HPC systems, enabling more efficient heat dissipation and a significant reduction in energy consumption.
Curious to see how it works? Check out our video by Elisabetta Boella (E4 HPC Product Specialist), for a closer look at this groundbreaking technology.
E4 Computer Engineering presented TEXTAROSSA at the HiPEAC international conference in Munich.
PSNC presented TEXTAROSSA at Supercomputing conference 2023.
The TEXTAROSSA project has been presented by E4 at the HPCAI Advisory Council (Lugano, Svizzera – 03-06/04/2023).
Machine Learning in general, and Deep Neural Networks (DNNs) in particular, have recently been shown to tolerate low-precision representations of their parameters.
This represents an opportunity to accelerate computations, reduce storage, and, most importantly, reduce power consumption. At the edge and on embedded devices, the latter is critical.
In neural networks, two game-changing factors are developing.
The RISC-V open instruction set architecture (ISA) enables for the seamless implementation of custom instruction sets. Second, several novel formats for real number arithmetic exist. In TextaRossa we aim to merge these two major components by developing an accelerator for mixed precision, employing one or more promising low-precision formats (e.g., Posit, bfloat). We aim to develop an enhancement to an original RISC-V ISA that allows for the computation such formats as well as the interoperability of these formats alongside the standard 32-bit IEEE Floats (a.k.a. binary32) or traditional fixed-point formats to provide a compact representation of real numbers with minimal to no accuracy deterioration and with a compression factor of 2 to 4 times. In TextaRossa we have two main paths in exploiting low-precision format.
The first one is the design by UNIPISA of an IP core for a lightweight PPU (Posit Processing Unit) to be connected to a 64b RISC-V processor in the form of a co-processor with an extension of the Instruction Set Architecture (ISA). We focus on the compression abilities of posits by providing a co-processor with only conversions in mind, called light PPU. We can convert binary32 floating point numbers to posit numbers with 16 and 8 bits. This co-processor can be paired with a RISCV-V core that already has a floating-point unit (e.g., Ariane 64b RISC-V) without interrupting the existing pipeline. On the other hand, we can use this unit to enable ALU computation of posit numbers with the posit-to-fixed conversion modules on a RISCV-V core that does not support floating-point.
The second one is the design by UNIPISA of a complete Posit Processing Unit (namely Full PPU, FPPU) that can be connected to a RISC-V processor core with a further extension of the ISA, adding the capabilities of complete posit arithmetic to such core. This approach enables us to deliver efficient real number arithmetic with 8 or 16 bits (thus reducing the bits used by a factor 4 or 2, compared to binary32 numbers), even in low-power processors that are not equipped with a traditional floating-point unit. Low power performance of the PPU coprocessors has been also validated by UNIPISA and POLIMI.
Leading partner: UNIPI
References
 |  | This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 956831. The JU receives support from the European Union’s Horizon 2020 research and innovation programme. |
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