NDC: Analyzing the impact of 3D-stacked memory+logic devices on MapReduce workloads

pre-printWhile Processing-in-Memory has been investigated for decades, it has not been embraced commercially. A number of emerging technologies have renewed interest in this topic. In particular, the emergence of 3D stacking and the imminent release of Micron's Hybrid Memory Cube device have made it more practical to move computation near memory. However, the literature is missing a detailed analysis of a killer application that can leverage a Near Data Computing (NDC) architecture. This paper focuses on in-memory MapReduce workloads that are commercially important and are especially suitable for NDC because of their embarrassing parallelism and largely localized memory accesses. The NDC architecture incorporates several simple processing cores on a separate, non-memory die in a 3D-stacked memory package; these cores can perform Map operations with efficient memory access and without hitting the bandwidth wall. This paper describes and evaluates a number of key elements necessary in realizing efficient NDC operation: (i) low-EPI cores, (ii) long daisy chains of memory devices, (iii) the dynamic activation of cores and SerDes links. Compared to a baseline that is heavily optimized for MapReduce execution, the NDC design yields up to 15X reduction in execution time and 18X reduction in system energy

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

LDM: Lineage-Aware Data Management in Multi-tier Storage Systems

We design and develop LDM, a novel data management solution to cater the needs of applications exhibiting the lineage property, i.e. in which the current writes are future reads. In such a class of applications, slow writes significantly hurt the over-all performance of jobs, i.e. current writes determine the fate of next reads. We believe that in a large scale shared production cluster, the issues associated due to data management can be mitigated at a way higher layer in the hierarchy of the I/O path, even before requests to data access are made. Contrary to the current solutions to data management which are mostly reactive and/or based on heuristics, LDM is both deterministic and pro-active. We develop block-graphs, which enable LDM to capture the complete time-based data-task dependency associations, therefore use it to perform life-cycle management through tiering of data blocks. LDM amalgamates the information from the entire data center ecosystem, right from the application code, to file system mappings, the compute and storage devices topology, etc. to make oracle-like deterministic data management decisions. With trace-driven experiments, LDM is able to achieve 29–52% reduction in over-all data center workload execution time. Moreover, by deploying LDM with extensive pre-processing creates efficient data consumption pipelines, which also reduces write and read delays significantly

Evaluating the benefits of key-value databases for scientific applications

The convergence of Big Data applications with High-Performance Computing requires new methodologies to store, manage and process large amounts of information. Traditional storage solutions are unable to scale and that results in complex coding strategies. For example, the brain atlas of the Human Brain Project has the challenge to process large amounts of high-resolution brain images. Given the computing needs, we study the effects of replacing a traditional storage system with a distributed Key-Value database on a cell segmentation application. The original code uses HDF5 files on GPFS through an intricate interface, imposing synchronizations. On the other hand, by using Apache Cassandra or ScyllaDB through Hecuba, the application code is greatly simplified. Thanks to the Key-Value data model, the number of synchronizations is reduced and the time dedicated to I/O scales when increasing the number of nodes.This project/research has received funding from the European Unions Horizon 2020 Framework Programme for Research and Innovation under the Speci c Grant Agreement No. 720270 (Human Brain Project SGA1) and the Speci c Grant Agreement No. 785907 (Human Brain Project SGA2). This work has also been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), and by Generalitat de Catalunya (contract 2017-SGR-1414).Postprint (author's final draft