Janus II: a new generation application-driven computer for spin-system simulations

This paper describes the architecture, the development and the implementation of Janus II, a new generation application-driven number cruncher optimized for Monte Carlo simulations of spin systems (mainly spin glasses). This domain of computational physics is a recognized grand challenge of high-performance computing: the resources necessary to study in detail theoretical models that can make contact with experimental data are by far beyond those available using commodity computer systems. On the other hand, several specific features of the associated algorithms suggest that unconventional computer architectures, which can be implemented with available electronics technologies, may lead to order of magnitude increases in performance, reducing to acceptable values on human scales the time needed to carry out simulation campaigns that would take centuries on commercially available machines. Janus II is one such machine, recently developed and commissioned, that builds upon and improves on the successful JANUS machine, which has been used for physics since 2008 and is still in operation today. This paper describes in detail the motivations behind the project, the computational requirements, the architecture and the implementation of this new machine and compares its expected performances with those of currently available commercial systems.Comment: 28 pages, 6 figure

Massively parallel simulations for disordered systems

Simulations of systems with quenched disorder are extremely demanding, suffering from the combined effect of slow relaxation and the need of performing the disorder average. As a consequence, new algorithms, improved implementations, and alternative and even purpose-built hardware are often instrumental for conducting meaningful studies of such systems. The ensuing demands regarding hardware availability and code complexity are substantial and sometimes prohibitive. We demonstrate how with a moderate coding effort leaving the overall structure of the simulation code unaltered as compared to a CPU implementation, very significant speed-ups can be achieved from a parallel code on GPU by mainly exploiting the trivial parallelism of the disorder samples and the near-trivial parallelism of the parallel tempering replicas. A combination of this massively parallel implementation with a careful choice of the temperature protocol for parallel tempering as well as efficient cluster updates allows us to equilibrate comparatively large systems with moderate computational resources.Comment: accepted for publication in EPJB, Topical issue - Recent advances in the theory of disordered system

Trends and outcome of neoadjuvant treatment for rectal cancer: A retrospective analysis and critical assessment of a 10-year prospective national registry on behalf of the Spanish Rectal Cancer Project

Introduction: Preoperative treatment and adequate surgery increase local control in rectal cancer. However, modalities and indications for neoadjuvant treatment may be controversial. Aim of this study was to assess the trends of preoperative treatment and outcomes in patients with rectal cancer included in the Rectal Cancer Registry of the Spanish Associations of Surgeons. Method: This is a STROBE-compliant retrospective analysis of a prospective database. All patients operated on with curative intention included in the Rectal Cancer Registry were included. Analyses were performed to compare the use of neoadjuvant/adjuvant treatment in three timeframes: I)2006–2009; II)2010–2013; III)2014–2017. Survival analyses were run for 3-year survival in timeframes I-II. Results: Out of 14, 391 patients, 8871 (61.6%) received neoadjuvant treatment. Long-course chemo/radiotherapy was the most used approach (79.9%), followed by short-course radiotherapy ± chemotherapy (7.6%). The use of neoadjuvant treatment for cancer of the upper third (15-11 cm) increased over time (31.5%vs 34.5%vs 38.6%, p = 0.0018). The complete regression rate slightly increased over time (15.6% vs 16% vs 18.5%; p = 0.0093); the proportion of patients with involved circumferential resection margins (CRM) went down from 8.2% to 7.3%and 5.5% (p = 0.0004). Neoadjuvant treatment significantly decreased positive CRM in lower third tumors (OR 0.71, 0.59–0.87, Cochrane-Mantel-Haenszel P = 0.0008). Most ypN0 patients also received adjuvant therapy. In MR-defined stage III patients, preoperative treatment was associated with significantly longer local-recurrence-free survival (p < 0.0001), and cancer-specific survival (p < 0.0001). The survival benefit was smaller in upper third cancers. Conclusion: There was an increasing trend and a potential overuse of neoadjuvant treatment in cancer of the upper rectum. Most ypN0 patients received postoperative treatment. Involvement of CRM in lower third tumors was reduced after neoadjuvant treatment. Stage III and MRcN + benefited the most

An FPGA-based supercomputer for statistical physics: The weird case of Janus

In this chapter we describe the Janus supercomputer, a massively parallel FPGA-based system optimized for the simulation of spin-glasses, theoretical models that describe the behavior of glassy materials. The custom architecture of Janus has been developed to meet the computational requirements of these models. Spin-glass simulations are performed using Monte Carlo methods that lead to algorithms characterized by (1) intrinsic parallelism allowing us to implement many Monte Carlo update engines within a single FPGA; (2) rather small data base (2 MByte) that can be stored on-chip, significantly boosting bandwidth and reducing latency. (3) need to generate a large number of good-quality long (≥ 32 bit) random numbers; (4) mostly integer arithmetic and bitwise logic operations. Careful tailoring of the architecture to the specific features of these algorithms has allowed us to embed up to 1024 special purpose cores within just one FPGA, so that simulations of systems that would take centuries on conventional architectures can be performed in just a few months

Spin glass simulations on the Janus architecture: A desperate quest for strong scaling

We describe Janus, an application-driven architecture for Monte Carlo simulations of spin glasses. Janus is a massively parallel architecture, based on reconfigurable FPGA nodes; it offers two orders of magnitude better performance than commodity systems for spin glass applications. The first generation Janus machine has been operational since early 2008; we are currently developing a new generation, that will be on line in early 2013. In this paper we present the Janus architecture, describe both implementations and compare their performances with those of commodity systems. © 2013 Springer-Verlag

Indução de fitoalexinas em cotilédones de soja em resposta a derivados de folhas de pitangueira Induction of phytoalexins in cotyledons of soybean in response to the derivatives of leaf surinan cherry

A demanda mundial por alimentos isentos de agrotóxicos tem impulsionado a pesquisa para a busca de métodos alternativos ao controle de patógenos em plantas. A ativação dos mecanismos de defesa com o uso de indutores vem demonstrando ser uma alternativa viável e promissora. O uso de extratos de plantas medicinais tem demonstrado capacidade para induzir a produção de fitoalexinas, como um mecanismo de defesa em plantas tratadas. O objetivo deste trabalho foi verificar o potencial da planta Eugenia uniflora L. (pitangueira) em induzir fitoalexinas em cotilédones de soja (Glycine max). Os derivados de extrato alcoólico, infusão, maceração e decocção, obtidos de folhas de pitangueira, foram usados nas concentrações de 0,1; 1; 10 e 40%, além de óleo essencial. Água foi utilizada como controle e quitosana (1%) como indutor de referência. Os preparados de pitangueira apresentaram capacidade de indução das fitoalexinas gliceolinas em cotilédones de soja, respondendo ao aumento das concentrações dos preparados. O óleo essencial apresentou destacável efeito na indução de fitoalexinas, sendo superior aos demais preparados. Quitosana induziu fitoalexinas em cotilédones de soja e pode ser utilizado em estudos similares como um indutor de referência.<br>The worldwide demand for food without pesticides has stimulated the research on alternative methods to control pathogens in plants. The activation of defense mechanisms by inductors seems a viable and promising alternative. The use of medicinal plants extracts has demonstrated capacity to induce the production of phytoalexins, as a mechanism of defense in treated plants. The objective of this research was to verify the potential of Eugenia uniflora L. (surinan cherry) to induce phytoalexins in cotyledons of soybean (Glycine max). The derivatives alcoholic extract, infusion, maceration and decoction, obtained of surinan cherry were used in the concentrations of 0.1; 1; 10 and 40%, beyond essential oil. Water was used for the control and chitosan (1%) as inductor reference. The preparations of surinan cherry presented capacity of induction of the phytoalexins glyceolin in cotyledons of soybean, with the increasing concentration of the preparations. The essential oil presents detachable effect in the induction of phytoalexins in relation to the other preparations. Chitosan induces phytoalexins in cotyledons soybean and can be used in similar studies as a reference inductor

Reconfigurable computing for Monte Carlo simulations: Results and prospects of the Janus project

We describe Janus, a massively parallel FPGA-based computer optimized for the simulation of spin glasses, theoretical models for the behavior of glassy materials. FPGAs (as compared to GPUs or many-core processors) provide a complementary approach to massively parallel computing. In particular, our model problem is formulated in terms of binary variables, and floating-point operations can be (almost) completely avoided. The FPGA architecture allows us to run many independent threads with almost no latencies in memory access, thus updating up to 1024 spins per cycle. We describe Janus in detail and we summarize the physics results obtained in four years of operation of this machine; we discuss two types of physics applications: long simulations on very large systems (which try to mimic and provide understanding about the experimental non-equilibrium dynamics), and low-temperature equilibrium simulations using an artificial parallel tempering dynamics. The time scale of our non-equilibrium simulations spans eleven orders of magnitude (from picoseconds to a tenth of a second). On the other hand, our equilibrium simulations are unprecedented both because of the low temperatures reached and for the large systems that we have brought to equilibrium. A finite-time scaling ansatz emerges from the detailed comparison of the two sets of simulations. Janus has made it possible to perform spin-glass simulations that would take several decades on more conventional architectures. The paper ends with an assessment of the potential of possible future versions of the Janus architecture, based on state-of-the-art technology