Leveraging disaggregated accelerators and non-volatile memories to improve the efficiency of modern datacenters

(English) Traditional data centers consist of computing nodes that possess all the resources physically attached. When there was the need to deal with more significant demands, the solution has been to either add more nodes (scaling out) or increase the capacity of existing ones (scaling-up). Workload requirements are traditionally fulfilled by selecting compute platforms from pools that better satisfy their average or maximum resource requirements depending on the price that the user is willing to pay. The amount of processor, memory, storage, and network bandwidth of a selected platform needs to meet or exceed the platform requirements of the workload. Beyond those explicitly required by the workload, additional resources are considered stranded resources (if not used) or bonus resources (if used). Meanwhile, workloads in all market segments have evolved significantly during the last decades. Today, workloads have a larger variety of requirements in terms of characteristics related to the computing platforms. Those workload new requirements include new technologies such as GPU, FPGA, NVMe, etc. These new technologies are more expensive and thus become more limited. It is no longer feasible to increase the number of resources according to potential peak demands, as this significantly raises the total cost of ownership. Software-Defined-Infrastructures (SDI), a new concept for the data center architecture, is being developed to address those issues. The main SDI proposition is to disaggregate all the resources over the fabric to enable the required flexibility. On SDI, instead of pools of computational nodes, the pools consist of individual units of resources (CPU, memory, FPGA, NVMe, GPU, etc.). When an application needs to be executed, SDI identifies the computational requirements and assembles all the resources required, creating a composite node. Resource disaggregation brings new challenges and opportunities that this thesis will explore. This thesis demonstrates that resource disaggregation brings opportunities to increase the efficiency of modern data centers. This thesis demonstrates that resource disaggregation may increase workloads' performance when sharing a single resource. Thus, needing fewer resources to achieve similar results. On the other hand, this thesis demonstrates how through disaggregation, aggregation of resources can be made, increasing a workload's performance. However, to take maximum advantage of those characteristics and flexibility, orchestrators must be aware of them. This thesis demonstrates how workload-aware techniques applied at the resource management level allow for improved quality of service leveraging resource disaggregation. Enabling resource disaggregation, this thesis demonstrates a reduction of up to 49% missed deadlines compared to a traditional schema. This reduction can rise up to 100% when enabling workload awareness. Moreover, this thesis demonstrates that GPU partitioning and disaggregation further enhances the data center flexibility. This increased flexibility can achieve the same results with half the resources. That is, with a single physical GPU partitioned and disaggregated, the same results can be achieved with 2 GPU disaggregated but not partitioned. Finally, this thesis demonstrates that resource fragmentation becomes key when having a limited set of heterogeneous resources, namely NVMe and GPU. For the case of an heterogeneous set of resources, and specifically when some of those resources are highly demanded but limited in quantity. That is, the situation where the demand for a resource is unexpectedly high, this thesis proposes a technique to minimize fragmentation that reduces deadlines missed compared to a disaggregation-aware policy of up to 86%.(Català) Els datacenters tradicionals consisteixen en un seguit de nodes computacionals que contenen al seu interior tots els recursos necessaris. Quan hi ha una necessitat de gestionar demandes superiors la solució era o afegir més nodes (scale-out) o incrementar la capacitat dels existents (scale-up). Els requisits de les aplicacions tradicionalment són satisfets seleccionant recursos de racks que satisfan millor el seu SLA basats o en la mitjana dels requisits o en el màxim possible, en funció del preu que l'usuari estigui disposat a pagar. La quantitat de processadors, memòria, disc, i banda d'ampla d'un rack necessita satisfer o excedir els requisits de l'aplicació. Els recursos addicionals als requerits per les aplicacions són considerats inactius (si no es fan servir) o addicionals (si es fan servir). Per altra banda, les aplicacions en tots els segments de mercat han evolucionat significativament en les últimes dècades. Avui en dia, les aplicacions tenen una gran varietat de requisits en termes de característiques que ha de tenir la infraestructura. Aquests nous requisits inclouen tecnologies com GPU, FPGA, NVMe, etc. Aquestes tecnologies són més cares i, per tant, més limitades. Ja no és factible incrementar el nombre de recursos segons el potencial pic de demanda, ja que això incrementa significativament el cost total de la infraestructura. Software-Defined Infrastructures és un nou concepte per a l'arquitectura de datacenters que s'està desenvolupant per pal·liar aquests problemes. La proposició principal de SDI és desagregar tots els recursos sobre la xarxa per garantir una major flexibilitat. Sota SDI, en comptes de racks de nodes computacionals, els racks consisteix en unitats individuals de recursos (CPU, memòria, FPGA, NVMe, GPU, etc). Quan una aplicació necessita executar, SDI identifica els requisits computacionals i munta una plataforma amb tots els recursos necessaris, creant un node composat. La desagregació de recursos porta nous reptes i oportunitats que s'exploren en aquesta tesi. Aquesta tesi demostra que la desagregació de recursos ens dona l'oportunitat d'incrementar l'eficiència dels datacenters moderns. Aquesta tesi demostra la desagregació pot incrementar el rendiment de les aplicacions. Però per treure el màxim partit a aquestes característiques i d'aquesta flexibilitat, els orquestradors n'han de ser conscient. Aquesta tesi demostra que aplicant tècniques conscients de l'aplicació aplicades a la gestió de recursos permeten millorar la qualitat del servei a través de la desagregació de recursos. Habilitar la desagregació de recursos porta a una reducció de fins al 49% els deadlines perduts comparat a una política tradicional. Aquesta reducció pot incrementar-se fins al 100% quan s'habilita la consciència de l'aplicació. A més a més, aquesta tesi demostra que el particionat de GPU combinat amb la desagregació millora encara més la flexibilitat. Aquesta millora permet aconseguir els mateixos resultats amb la meitat de recursos. És a dir, amb una sola GPU física particionada i desagregada, els mateixos resultats són obtinguts que utilitzant-ne dues desagregades però no particionades. Finalment, aquesta tesi demostra que la gestió de la fragmentació de recursos és una peça clau quan la quantitat de recursos és limitada en un conjunt heterogeni de recursos. Pel cas d'un conjunt heterogeni de recursos, i especialment quan aquests recursos tenen molta demanda però són limitats en quantitat. És a dir, quan la demanda pels recursos és inesperadament alta, aquesta tesi proposa una tècnica minimitzant la fragmentació que redueix els deadlines perduts comparats a una política de desagregació de fins al 86%.Arquitectura de computador

ALOJA: A framework for benchmarking and predictive analytics in Hadoop deployments

This article presents the ALOJA project and its analytics tools, which leverages machine learning to interpret Big Data benchmark performance data and tuning. ALOJA is part of a long-term collaboration between BSC and Microsoft to automate the characterization of cost-effectiveness on Big Data deployments, currently focusing on Hadoop. Hadoop presents a complex run-time environment, where costs and performance depend on a large number of configuration choices. The ALOJA project has created an open, vendor-neutral repository, featuring over 40,000 Hadoop job executions and their performance details. The repository is accompanied by a test-bed and tools to deploy and evaluate the cost-effectiveness of different hardware configurations, parameters and Cloud services. Despite early success within ALOJA, a comprehensive study requires automation of modeling procedures to allow an analysis of large and resource-constrained search spaces. The predictive analytics extension, ALOJA-ML, provides an automated system allowing knowledge discovery by modeling environments from observed executions. The resulting models can forecast execution behaviors, predicting execution times for new configurations and hardware choices. That also enables model-based anomaly detection or efficient benchmark guidance by prioritizing executions. In addition, the community can benefit from ALOJA data-sets and framework to improve the design and deployment of Big Data applications.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). This work is partially supported by the Ministry of Economy of Spain under contracts TIN2012-34557 and 2014SGR1051.Peer ReviewedPostprint (published version

ALOJA-ML: a framework for automating characterization and knowledge discovery in Hadoop deployments

This article presents ALOJA-Machine Learning (ALOJA-ML) an extension to the ALOJA project that uses machine learning techniques to interpret Hadoop benchmark performance data and performance tuning; here we detail the approach, efficacy of the model and initial results. The ALOJA-ML project is the latest phase of a long-term collaboration between BSC and Microsoft, to automate the characterization of cost-effectiveness on Big Data deployments, focusing on Hadoop. Hadoop presents a complex execution environment, where costs and performance depends on a large number of software (SW) configurations and on multiple hardware (HW) deployment choices. Recently the ALOJA project presented an open, vendor-neutral repository, featuring over 16.000 Hadoop executions. These results are accompanied by a test bed and tools to deploy and evaluate the cost-effectiveness of the different hardware configurations, parameter tunings, and Cloud services. Despite early success within ALOJA from expert-guided benchmarking, it became clear that a genuinely comprehensive study requires automation of modeling procedures to allow a systematic analysis of large and resource-constrained search spaces. ALOJA-ML provides such an automated system allowing knowledge discovery by modeling Hadoop executions from observed benchmarks across a broad set of configuration parameters. The resulting empirically-derived performance models can be used to forecast execution behavior of various workloads; they allow a-priori prediction of the execution times for new configurations and HW choices and they offer a route to model-based anomaly detection. In addition, these models can guide the benchmarking exploration efficiently, by automatically prioritizing candidate future benchmark tests. Insights from ALOJA-ML's models can be used to reduce the operational time on clusters, speed-up the data acquisition and knowledge discovery process, and importantly, reduce running costs. In addition to learning from the methodology presented in this work, the community can benefit in general from ALOJA data-sets, framework, and derived insights to improve the design and deployment of Big Data applications.This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 re- search and innovation programme (grant agreement No 639595). This work is partially supported by the Ministry of Economy of Spain under contracts TIN2012-34557 and 2014SGR105Peer ReviewedPostprint (published version

Four-week rapamycin treatment improves muscular dystrophy in a fukutin-deficient mouse model of dystroglycanopathy

Tissue mass-normalized values of cytochrome C reduced in vitro by succinate dehydrogenase from homogenized TAs of VEH- or RAPA-treated LC and KO mice. Two-way ANOVA. (PDF 291 kb

Challenges and Opportunities for RISC-V Architectures towards Genomics-based Workloads

The use of large-scale supercomputing architectures is a hard requirement for scientific computing Big-Data applications. An example is genomics analytics, where millions of data transformations and tests per patient need to be done to find relevant clinical indicators. Therefore, to ensure open and broad access to high-performance technologies, governments, and academia are pushing toward the introduction of novel computing architectures in large-scale scientific environments. This is the case of RISC-V, an open-source and royalty-free instruction-set architecture. To evaluate such technologies, here we present the Variant-Interaction Analytics use case benchmarking suite and datasets. Through this use case, we search for possible genetic interactions using computational and statistical methods, providing a representative case for heavy ETL (Extract, Transform, Load) data processing. Current implementations are implemented in x86-based supercomputers (e.g. MareNostrum-IV at the Barcelona Supercomputing Center (BSC)), and future steps propose RISC-V as part of the next MareNostrum generations. Here we describe the Variant Interaction Use Case, highlighting the characteristics leveraging high-performance computing, indicating the caveats and challenges towards the next RISC-V developments and designs to come from a first comparison between x86 and RISC-V architectures on real Variant Interaction executions over real hardware implementations

Comparing ST-segment elevation myocardial infarction care between patients residing in central and remote locations: a retrospective case series.

People who experience an ST-elevation myocardial infarction (STEMI) due to an occluded coronary artery require prompt treatment. Treatments to open a blocked artery are called reperfusion therapies (RTs), and can include intravenous pharmacological thrombolysis (TL) or primary percutaneous coronary intervention (pPCI) in a cardiac catheterisation laboratory (cath lab). Optimal RT (ORT) with pPCI or TL reduces morbidity and mortality. In remote areas, a number of geographical and organisational barriers may influence access to ORT. These are not well understood, and the exact proportion of patients who receive ORT - and the relationship to time of day and remoteness from the cardiac cath lab - is unknown. The aim of this retrospective study was to compare the characteristics of ORT delivery in central and remote locations in the north of Scotland, and to identify potential barriers to optimal care with a view to service redesign. The study was set in the north of Scotland. All patients who attended hospital with a STEMI between March 2014 and April 2015 were identified from national coding data. A data collection form was developed by the research team in several iterative stages. Clinical details were collected retrospectively from patients' discharge letters. Data included treatment location, date of admission, distance of patient from the cath lab, route of access to health care, left ventricular function and RT received. Distance of patients from the cath lab was described as remote if they were more than ninety minutes of driving time from the cardiac cath lab, and described as central if they were ninety minutes or less of driving time from the regional centre. For patients who made contact in a pre-hospital setting, ORT was defined as pre-hospital TL (PHT) or pPCI. For patients who self-presented to the hospital first, ORT was defined as in-hospital TL or pPCI. Data were described as mean (standard deviation) as appropriate. Chi-squared and student's t-test were used as appropriate. Each case was reviewed to determine if ORT was received; if ORT was not received, the reasons for this were recorded to identify potentially modifiable barriers. Of the 627 acute myocardial infarction patients initially identified, 131 had a STEMI, and the others were non-STEMI. From this STEMI cohort, 82 (62%) patients were classed as central and 49 (38%) were remote. In terms of initial therapy, 26 (20%) received pPCI, 19 (15%) received PHTs, 52 (40%) received in-hospital TL, while 33 (25%) received no initial RT. ORT was received by 53 (65%) central and 20 (41%) remote patients; chi-squared = 7.05, degrees of freedom = 130, p < 0.01).Several recurring barriers were identified. This study has therefore demonstrated a significant health inequality between the treatment of STEMI in remote locations compared to central locations. Potential barriers identified include staffing availability and training, public awareness and inter-hospital communication. This suggests that there remain significant opportunities to improve STEMI care for people living in the north of Scotland

Challenges and opportunities for RISC-V architectures towards genomics-based workloads

The use of large-scale supercomputing architectures is a hard requirement for scientific computing Big-Data applications. An example is genomics analytics, where millions of data transformations and tests per patient need to be done to find relevant clinical indicators. Therefore, to ensure open and broad access to high-performance technologies, governments, and academia are pushing toward the introduction of novel computing architectures in large-scale scientific environments. This is the case of RISC-V, an open-source and royalty-free instruction-set architecture. To evaluate such technologies, here we present the Variant-Interaction Analytics use case benchmarking suite and datasets. Through this use case, we search for possible genetic interactions using computational and statistical methods, providing a representative case for heavy ETL (Extract, Transform, Load) data processing. Current implementations are implemented in x86-based supercomputers (e.g. MareNostrum-IV at the Barcelona Supercomputing Center (BSC)), and future steps propose RISC-V as part of the next MareNostrum generations. Here we describe the Variant Interaction Use Case, highlighting the characteristics leveraging high-performance computing, indicating the caveats and challenges towards the next RISC-V developments and designs to come from a first comparison between x86 and RISC-V architectures on real Variant Interaction executions over real hardware implementations.This work has been partially financed by the European Commission (EU-HORIZON NEARDATA GA.101092644, VITAMIN-V GA.101093062), the MEEP Project which received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 946002. The JU receives support from the European Union’s Horizon 2020 research and innovation program and Spain, Croatia and Turkey. Also by the Spanish Ministry of Science (MICINN) under scholarship BES-2017-081635, the Research State Agency (AEI) and European Regional Development Funds (ERDF/FEDER) under DALEST grant agreement PID2021-126248OBI00, MCIN/AEI/10.13039/ 501100011033/FEDER and PID GA PID2019-107255GB-C21, and the Generalitat de Catalunya (AGAUR) under grant agreements 2021-SGR-00478, 2021-SGR-01626 and ”FSE Invertint en el teu futur”.Peer ReviewedPostprint (author's final draft

3 years of liraglutide versus placebo for type 2 diabetes risk reduction and weight management in individuals with prediabetes: a randomised, double-blind trial

Background: Liraglutide 3·0 mg was shown to reduce bodyweight and improve glucose metabolism after the 56-week period of this trial, one of four trials in the SCALE programme. In the 3-year assessment of the SCALE Obesity and Prediabetes trial we aimed to evaluate the proportion of individuals with prediabetes who were diagnosed with type 2 diabetes. Methods: In this randomised, double-blind, placebo-controlled trial, adults with prediabetes and a body-mass index of at least 30 kg/m2, or at least 27 kg/m2 with comorbidities, were randomised 2:1, using a telephone or web-based system, to once-daily subcutaneous liraglutide 3·0 mg or matched placebo, as an adjunct to a reduced-calorie diet and increased physical activity. Time to diabetes onset by 160 weeks was the primary outcome, evaluated in all randomised treated individuals with at least one post-baseline assessment. The trial was conducted at 191 clinical research sites in 27 countries and is registered with ClinicalTrials.gov, number NCT01272219. Findings: The study ran between June 1, 2011, and March 2, 2015. We randomly assigned 2254 patients to receive liraglutide (n=1505) or placebo (n=749). 1128 (50%) participants completed the study up to week 160, after withdrawal of 714 (47%) participants in the liraglutide group and 412 (55%) participants in the placebo group. By week 160, 26 (2%) of 1472 individuals in the liraglutide group versus 46 (6%) of 738 in the placebo group were diagnosed with diabetes while on treatment. The mean time from randomisation to diagnosis was 99 (SD 47) weeks for the 26 individuals in the liraglutide group versus 87 (47) weeks for the 46 individuals in the placebo group. Taking the different diagnosis frequencies between the treatment groups into account, the time to onset of diabetes over 160 weeks among all randomised individuals was 2·7 times longer with liraglutide than with placebo (95% CI 1·9 to 3·9, p&lt;0·0001), corresponding with a hazard ratio of 0·21 (95% CI 0·13–0·34). Liraglutide induced greater weight loss than placebo at week 160 (–6·1 [SD 7·3] vs −1·9% [6·3]; estimated treatment difference −4·3%, 95% CI −4·9 to −3·7, p&lt;0·0001). Serious adverse events were reported by 227 (15%) of 1501 randomised treated individuals in the liraglutide group versus 96 (13%) of 747 individuals in the placebo group. Interpretation: In this trial, we provide results for 3 years of treatment, with the limitation that withdrawn individuals were not followed up after discontinuation. Liraglutide 3·0 mg might provide health benefits in terms of reduced risk of diabetes in individuals with obesity and prediabetes. Funding: Novo Nordisk, Denmark