Partition Around Medoids Clustering on the Intel Xeon Phi Many-Core Coprocessor

Abstract. The paper touches upon the problem of implementation Partition Around Medoids (PAM) clustering algorithm for the Intel Many Integrated Core architecture. PAM is a form of well-known k-Medoids clustering algorithm and is applied in various subject domains, e.g. bioinformatics, text analysis, intelligent transportation systems, etc. An optimized version of PAM for the Intel Xeon Phi coprocessor is introduced where OpenMP parallelizing technology, loop vectorization, tiling technique and efficient distance matrix computation for Euclidean metric are used. Experimental results for different data sets confirm the efficiency of the proposed algorithm

GekkoFS: A temporary distributed file system for HPC applications

We present GekkoFS, a temporary, highly-scalable burst buffer file system which has been specifically optimized for new access patterns of data-intensive High-Performance Computing (HPC) applications. The file system provides relaxed POSIX semantics, only offering features which are actually required by most (not all) applications. It is able to provide scalable I/O performance and reaches millions of metadata operations already for a small number of nodes, significantly outperforming the capabilities of general-purpose parallel file systems.The work has been funded by the German Research Foundation (DFG) through the ADA-FS project as part of the Priority Programme 1648. It is also supported by the Spanish Ministry of Science and Innovation (TIN2015–65316), the Generalitat de Catalunya (2014–SGR–1051), as well as the European Union’s Horizon 2020 Research and Innovation Programme (NEXTGenIO, 671951) and the European Comission’s BigStorage project (H2020-MSCA-ITN-2014-642963). This research was conducted using the supercomputer MOGON II and services offered by the Johannes Gutenberg University Mainz.Peer ReviewedPostprint (author's final draft

Mammut: High-level management of system knobs and sensors

Managing low-level architectural features for controlling performance and power consumption is a growing demand in the parallel computing community. Such features include, but are not limited to: energy profiling, platform topology analysis, CPU cores disabling and frequency scaling. However, these low-level mechanisms are usually managed by specific tools, without any interaction between each other, thus hampering their usability. More important, most existing tools can only be used through a command line interface and they do not provide any API. Moreover, in most cases, they only allow monitoring and managing the same machine on which the tools are used. MAMMUT provides and integrates architectural management utilities through a high-level and easy-to-use object-oriented interface. By using MAMMUT, is possible to link together different collected information and to exploit them on both local and remote systems, to build architecture-aware applications

ReStore: In-Memory REplicated STORagE for Rapid Recovery in Fault-Tolerant Algorithms

Fault-tolerant distributed applications require mechanisms to recover data lost via a process failure. On modern cluster systems it is typically impractical to request replacement resources after such a failure. Therefore, applications have to continue working with the remaining resources. This requires redistributing the workload and that the non-failed processes reload data. We present an algorithmic framework and its C++ library implementation ReStore for MPI programs that enables recovery of data after process failures. By storing all required data in memory via an appropriate data distribution and replication, recovery is substantially faster than with standard checkpointing schemes that rely on a parallel file system. As the application developer can specify which data to load, we also support shrinking recovery instead of recovery using spare compute nodes. We evaluate ReStore in both controlled, isolated environments and real applications. Our experiments show loading times of lost input data in the range of milliseconds on up to 24576 processors and a substantial speedup of the recovery time for the fault-tolerant version of a widely used bioinformatics application

Persistent Kernels for Iterative Memory-bound GPU Applications

Iterative memory-bound solvers commonly occur in HPC codes. Typical GPU implementations have a loop on the host side that invokes the GPU kernel as much as time/algorithm steps there are. The termination of each kernel implicitly acts as the barrier required after advancing the solution every time step. We propose a scheme for running memory-bound iterative GPU kernels: PERsistent KernelS (PERKS). In this scheme the time loop is moved inside a persistent kernel, and device-wide barriers are used for synchronization. We then reduce the traffic to device memory by caching a subset of the output in each time step in registers and shared memory to be used as input for the following time step. PERKS can be generalized to any iterative solver: they are largely independent of the solver's implementation. We explain the design principle of PERKS and demonstrate the effectiveness of PERKS for a wide range of iterative 2D/3D stencil benchmarks (geometric mean speedup of $2.29$x in small domains and $1.53$x in large domains), and a Krylov subspace solver (geometric mean speedup of $4.67$x in smaller SpMV datasets from SuiteSparse and $1.39$x in larger SpMV datasets, for conjugate gradient)

Bandwidth and Wavefront Reduction for Static Variable Ordering in Symbolic Model Checking

We demonstrate the applicability of bandwidth and wavefront reduction algorithms to static variable ordering. In symbolic model checking event locality plays a major role in time and memory usage. For example, in Petri nets event locality can be captured by dependency matrices, where nonzero entries indicate whether a transition modifies a place. The quality of event locality has been expressed as a metric called (weighted) event span. The bandwidth of a matrix is a metric indicating the distance of nonzero elements to the diagonal. Wavefront is a metric indicating the degree of nonzeros on one end of the diagonal of the matrix. Bandwidth and wavefront are well studied metrics used in sparse matrix solvers. \ud In this work we prove that span is limited by twice the bandwidth of a matrix. This observation makes bandwidth reduction algorithms useful for obtaining good variable orders. One major issue we address is that the reduction algorithms can only be applied on symmetric matrices, while the dependency matrices are asymmetric. We show that the Sloan algorithm executed on the total graph of the adjacency graph gives the best variable orders. Practically, we demonstrate that our work allows to call standard sparse matrix operations in Boost and ViennaCL, computing very good static variable orders in milliseconds. Future work is promising, because a whole new spectrum of more off-the-shelf algorithms, including metaheuristic ones, become available for variable ordering

GekkoFS: A temporary burst buffer file system for HPC applications

Many scientific fields increasingly use high-performance computing (HPC) to process and analyze massive amounts of experimental data while storage systems in today’s HPC environments have to cope with new access patterns. These patterns include many metadata operations, small I/O requests, or randomized file I/O, while general-purpose parallel file systems have been optimized for sequential shared access to large files. Burst buffer file systems create a separate file system that applications can use to store temporary data. They aggregate node-local storage available within the compute nodes or use dedicated SSD clusters and offer a peak bandwidth higher than that of the backend parallel file system without interfering with it. However, burst buffer file systems typically offer many features that a scientific application, running in isolation for a limited amount of time, does not require. We present GekkoFS, a temporary, highly-scalable file system which has been specifically optimized for the aforementioned use cases. GekkoFS provides relaxed POSIX semantics which only offers features which are actually required by most (not all) applications. GekkoFS is, therefore, able to provide scalable I/O performance and reaches millions of metadata operations already for a small number of nodes, significantly outperforming the capabilities of common parallel file systems.Peer ReviewedPostprint (author's final draft

LenticularFS: Scalable filesystem for the cloud

The Hadoop platform is the most common solution to handle the explosion of big-data that both companies and research institutions are facing. In order to store such data, the Hadoop platform provides HDFS, a scalable distributed filesystem, which runs on commodity hardware and enables linear scalability by adding new storage nodes. While storage capacity of the system can be increased by adding new storage nodes, the component that handles metadata for the filesystem, the namenode, is a single point of failure and cannot easily replaced or linearly scaled. The Hops projects provides an alternative implementation of the namenode, which increases performance and scalability by storing metadata on an external distributed NewSQL database called MySQL Cluster. With the new architecture, the system is much more scalable and can transparently manage the failover of namenodes, which are now stateless components. HopsFS is, however, still limited to running within a single datacenter, which can cause severe outages in case the entire datacenter becomes unavailable. Cloud native storage systems, such as Amazon’s Simple Storage Service (S3), solve this problem by replicating data across different, geographically distant datacenters, so that the failure of any given zone does not cause data unavailability. The objective of this thesis is to enable HopsFS to work across geographical regions while, as far as possible, maintaining the semantics of a POSIX-style hierarchical filesystem. We leverage asynchronous replication functionality provided by MySQL Cluster to obtain replication of metadata across geographical regions and we present a detailed analysis on how to maintain the consistency properties of HDFS in such an environment. Furthermore, we analyze the issue of split brain scenarios and propose a way for namenodes to detect this condition and continue operating correctly. Finally, we discuss the changes to the codebase which are required to implement the proposed plan