Evaluating Emerging CXL-enabled Memory Pooling for HPC Systems

Current HPC systems provide memory resources that are statically configured and tightly coupled with compute nodes. However, workloads on HPC systems are evolving. Diverse workloads lead to a need for configurable memory resources to achieve high performance and utilization. In this study, we evaluate a memory subsystem design leveraging CXL-enabled memory pooling. Two promising use cases of composable memory subsystems are studied -- fine-grained capacity provisioning and scalable bandwidth provisioning. We developed an emulator to explore the performance impact of various memory compositions. We also provide a profiler to identify the memory usage patterns in applications and their optimization opportunities. Seven scientific and six graph applications are evaluated on various emulated memory configurations. Three out of seven scientific applications had less than 10% performance impact when the pooled memory backed 75% of their memory footprint. The results also show that a dynamically configured high-bandwidth system can effectively support bandwidth-intensive unstructured mesh-based applications like OpenFOAM. Finally, we identify interference through shared memory pools as a practical challenge for adoption on HPC systems.Comment: 10 pages, 13 figures. Accepted for publication in Workshop on Memory Centric High Performance Computing (MCHPC'22) at SC2

Run-time parallelization and scheduling of loops

The class of problems that can be effectively compiled by parallelizing compilers is discussed. This is accomplished with the doconsider construct which would allow these compilers to parallelize many problems in which substantial loop-level parallelism is available but cannot be detected by standard compile-time analysis. We describe and experimentally analyze mechanisms used to parallelize the work required for these types of loops. In each of these methods, a new loop structure is produced by modifying the loop to be parallelized. We also present the rules by which these loop transformations may be automated in order that they be included in language compilers. The main application area of the research involves problems in scientific computations and engineering. The workload used in our experiment includes a mixture of real problems as well as synthetically generated inputs. From our extensive tests on the Encore Multimax/320, we have reached the conclusion that for the types of workloads we have investigated, self-execution almost always performs better than pre-scheduling. Further, the improvement in performance that accrues as a result of global topological sorting of indices as opposed to the less expensive local sorting, is not very significant in the case of self-execution

Capacity Planning of a Commodity Cluster in an Academic Environment: A Case Study

In this paper, the design of a simulation model for evaluating two alternative supercomputer configurations in an academic environment is presented. The workload is analyzed and modeled, and its effect on the relative performance of both systems is studied. The Integrated Capacity Planning Environment (ICPE) toolkit, developed for commodity cluster capacity planning, is successfully applied to the target environment. The ICPE is a tool for workload modeling, simulation modeling, and what-if analysis. A new characterization strategy is applied to the workload to more accurately model commodity cluster work- loads. Through what-if analysis, the sensitivity of the baseline system performance to workload change, and also the relative performance of the two proposed alternative systems are compared and evaluated. This case study demonstrates the usefulness of the methodology and the applicability of the tools in gauging system capacity and making design decisions

Load Balancer using Whale-Earthworm Optimization for Efficient Resource Scheduling in the IoT-Fog-Cloud Framework

Cloud-Fog environment is useful in offering optimized services to customers in their daily routine tasks. With the exponential usage of IoT devices, a huge scale of data is generated. Different service providers use optimization scheduling approaches to optimally allocate the scarce resources in the Fog computing environment to meet job deadlines. This study introduces the Whale-EarthWorm Optimization method (WEOA), a powerful hybrid optimization method for improving resource management in the Cloud-Fog environment. Striking a balance between exploration and exploitation of these approaches is difficult, if only Earthworm or Whale optimization methods are used. Earthworm technique can result in inefficiency due to its investigations and additional overhead, whereas Whale algorithm, may leave scope for improvement in finding the optimal solutions using its exploitation.  This research introduces an efficient task allocation method as a novel load balancer. It leverages an enhanced exploration phase inspired by the Earthworm algorithm and an improved exploitation phase inspired by the Whale algorithm to manage the optimization process. It shows a notable performance enhancement, with a 6% reduction in response time, a 2% decrease in cost, and a 2% improvement in makespan over EEOA. Furthermore, when compared to other approaches like h-DEWOA, CSDEO, CSPSO, and BLEMO, the proposed method achieves remarkable results, with response time reductions of up to 82%, cost reductions of up to 75%, and makespan improvements of up to 80%

Learning workload behaviour models from monitored time-series for resource estimation towards data center optimization

In recent years there has been an extraordinary growth of the demand of Cloud Computing resources executed in Data Centers. Modern Data Centers are complex systems that need management. As distributed computing systems grow, and workloads benefit from such computing environments, the management of such systems increases in complexity. The complexity of resource usage and power consumption on cloud-based applications makes the understanding of application behavior through expert examination difficult. The difficulty increases when applications are seen as "black boxes", where only external monitoring can be retrieved. Furthermore, given the different amount of scenarios and applications, automation is required. To deal with such complexity, Machine Learning methods become crucial to facilitate tasks that can be automatically learned from data. Firstly, this thesis proposes an unsupervised learning technique to learn high level representations from workload traces. Such technique provides a fast methodology to characterize workloads as sequences of abstract phases. The learned phase representation is validated on a variety of datasets and used in an auto-scaling task where we show that it can be applied in a production environment, achieving better performance than other state-of-the-art techniques. Secondly, this thesis proposes a neural architecture, based on Sequence-to-Sequence models, that provides the expected resource usage of applications sharing hardware resources. The proposed technique provides resource managers the ability to predict resource usage over time as well as the completion time of the running applications. The technique provides lower error predicting usage when compared with other popular Machine Learning methods. Thirdly, this thesis proposes a technique for auto-tuning Big Data workloads from the available tunable parameters. The proposed technique gathers information from the logs of an application generating a feature descriptor that captures relevant information from the application to be tuned. Using this information we demonstrate that performance models can generalize up to a 34% better when compared with other state-of-the-art solutions. Moreover, the search time to find a suitable solution can be drastically reduced, with up to a 12x speedup and almost equal quality results as modern solutions. These results prove that modern learning algorithms, with the right feature information, provide powerful techniques to manage resource allocation for applications running in cloud environments. This thesis demonstrates that learning algorithms allow relevant optimizations in Data Center environments, where applications are externally monitored and careful resource management is paramount to efficiently use computing resources. We propose to demonstrate this thesis in three areas that orbit around resource management in server environmentsEls Centres de Dades (Data Centers) moderns són sistemes complexos que necessiten ser gestionats. A mesura que creixen els sistemes de computació distribuïda i les aplicacions es beneficien d’aquestes infraestructures, també n’augmenta la seva complexitat. La complexitat que implica gestionar recursos de còmput i d’energia en sistemes de computació al núvol fa difícil entendre el comportament de les aplicacions que s'executen de manera manual. Aquesta dificultat s’incrementa quan les aplicacions s'observen com a "caixes negres", on només es poden monitoritzar algunes mètriques de les caixes de manera externa. A més, degut a la gran varietat d’escenaris i aplicacions, és necessari automatitzar la gestió d'aquests recursos. Per afrontar-ne el repte, l'aprenentatge automàtic juga un paper cabdal que facilita aquestes tasques, que poden ser apreses automàticament en base a dades prèvies del sistema que es monitoritza. Aquesta tesi demostra que els algorismes d'aprenentatge poden aportar optimitzacions molt rellevants en la gestió de Centres de Dades, on les aplicacions són monitoritzades externament i la gestió dels recursos és de vital importància per a fer un ús eficient de la capacitat de còmput d'aquests sistemes. En primer lloc, aquesta tesi proposa emprar aprenentatge no supervisat per tal d’aprendre representacions d'alt nivell a partir de traces d'aplicacions. Aquesta tècnica ens proporciona una metodologia ràpida per a caracteritzar aplicacions vistes com a seqüències de fases abstractes. La representació apresa de fases és validada en diferents “datasets” i s'aplica a la gestió de tasques d'”auto-scaling”, on es conclou que pot ser aplicable en un medi de producció, aconseguint un millor rendiment que altres mètodes de vanguardia. En segon lloc, aquesta tesi proposa l'ús de xarxes neuronals, basades en arquitectures “Sequence-to-Sequence”, que proporcionen una estimació dels recursos usats per aplicacions que comparteixen recursos de hardware. La tècnica proposada facilita als gestors de recursos l’habilitat de predir l'ús de recursos a través del temps, així com també una estimació del temps de còmput de les aplicacions. Tanmateix, redueix l’error en l’estimació de recursos en comparació amb d’altres tècniques populars d'aprenentatge automàtic. Per acabar, aquesta tesi introdueix una tècnica per a fer “auto-tuning” dels “hyper-paràmetres” d'aplicacions de Big Data. Consisteix així en obtenir informació dels “logs” de les aplicacions, generant un vector de característiques que captura informació rellevant de les aplicacions que s'han de “tunejar”. Emprant doncs aquesta informació es valida que els ”Regresors” entrenats en la predicció del rendiment de les aplicacions són capaços de generalitzar fins a un 34% millor que d’altres “Regresors” de vanguàrdia. A més, el temps de cerca per a trobar una bona solució es pot reduir dràsticament, aconseguint un increment de millora de fins a 12 vegades més dels resultats de qualitat en contraposició a alternatives modernes. Aquests resultats posen de manifest que els algorismes moderns d'aprenentatge automàtic esdevenen tècniques molt potents per tal de gestionar l'assignació de recursos en aplicacions que s'executen al núvol.Arquitectura de computador

Decentralized dynamic task allocation for UAVs with limited communication range

We present the Limited-range Online Routing Problem (LORP), which involves a team of Unmanned Aerial Vehicles (UAVs) with limited communication range that must autonomously coordinate to service task requests. We first show a general approach to cast this dynamic problem as a sequence of decentralized task allocation problems. Then we present two solutions both based on modeling the allocation task as a Markov Random Field to subsequently assess decisions by means of the decentralized Max-Sum algorithm. Our first solution assumes independence between requests, whereas our second solution also considers the UAVs' workloads. A thorough empirical evaluation shows that our workload-based solution consistently outperforms current state-of-the-art methods in a wide range of scenarios, lowering the average service time up to 16%. In the best-case scenario there is no gap between our decentralized solution and centralized techniques. In the worst-case scenario we manage to reduce by 25% the gap between current decentralized and centralized techniques. Thus, our solution becomes the method of choice for our problem

Efficient Implementation of Stochastic Inference on Heterogeneous Clusters and Spiking Neural Networks

Neuromorphic computing refers to brain inspired algorithms and architectures. This paradigm of computing can solve complex problems which were not possible with traditional computing methods. This is because such implementations learn to identify the required features and classify them based on its training, akin to how brains function. This task involves performing computation on large quantities of data. With this inspiration, a comprehensive multi-pronged approach is employed to study and efficiently implement neuromorphic inference model using heterogeneous clusters to address the problem using traditional Von Neumann architectures and by developing spiking neural networks (SNN) for native and ultra-low power implementation. In this regard, an extendable high-performance computing (HPC) framework and optimizations are proposed for heterogeneous clusters to modularize complex neuromorphic applications in a distributed manner. To achieve best possible throughput and load balancing for such modularized architectures a set of algorithms are proposed to suggest the optimal mapping of different modules as an asynchronous pipeline to the available cluster resources while considering the complex data dependencies between stages. On the other hand, SNNs are more biologically plausible and can achieve ultra-low power implementation due to its sparse spike based communication, which is possible with emerging non-Von Neumann computing platforms. As a significant progress in this direction, spiking neuron models capable of distributed online learning are proposed. A high performance SNN simulator (SpNSim) is developed for simulation of large scale mixed neuron model networks. An accompanying digital hardware neuron RTL is also proposed for efficient real time implementation of SNNs capable of online learning. Finally, a methodology for mapping probabilistic graphical model to off-the-shelf neurosynaptic processor (IBM TrueNorth) as a stochastic SNN is presented with ultra-low power consumption