3,208 research outputs found
CRAID: Online RAID upgrades using dynamic hot data reorganization
Current algorithms used to upgrade RAID arrays typically require large amounts of data to be migrated, even those that move only the minimum amount of data required to keep a balanced data load. This paper presents CRAID, a self-optimizing RAID array that performs an online block reorganization of frequently used, long-term accessed data in order to reduce this migration even further. To achieve this objective, CRAID tracks frequently used, long-term data blocks and copies them to a dedicated partition spread across all the disks in the array. When new disks are added, CRAID only needs to extend this process to the new devices to redistribute this partition, thus greatly reducing the overhead of the upgrade process. In addition, the reorganized access patterns within this partition improve the array’s performance, amortizing the copy overhead and allowing CRAID to offer a performance competitive with traditional RAIDs.
We describe CRAID’s motivation and design and we evaluate it by replaying seven real-world workloads including a file server, a web server and a user share. Our experiments show that CRAID can successfully detect hot data variations and begin using new disks as soon as they are added to the array. Also, the usage of a dedicated
partition improves the sequentiality of relevant data access, which amortizes the cost of reorganizations. Finally, we prove that a full-HDD CRAID array with a small distributed partition (<1.28% per disk) can compete in performance with an ideally restriped RAID-5 and a hybrid RAID-5 with a small SSD cache.Peer ReviewedPostprint (published version
Hybrid static/dynamic scheduling for already optimized dense matrix factorization
We present the use of a hybrid static/dynamic scheduling strategy of the task
dependency graph for direct methods used in dense numerical linear algebra.
This strategy provides a balance of data locality, load balance, and low
dequeue overhead. We show that the usage of this scheduling in communication
avoiding dense factorization leads to significant performance gains. On a 48
core AMD Opteron NUMA machine, our experiments show that we can achieve up to
64% improvement over a version of CALU that uses fully dynamic scheduling, and
up to 30% improvement over the version of CALU that uses fully static
scheduling. On a 16-core Intel Xeon machine, our hybrid static/dynamic
scheduling approach is up to 8% faster than the version of CALU that uses a
fully static scheduling or fully dynamic scheduling. Our algorithm leads to
speedups over the corresponding routines for computing LU factorization in well
known libraries. On the 48 core AMD NUMA machine, our best implementation is up
to 110% faster than MKL, while on the 16 core Intel Xeon machine, it is up to
82% faster than MKL. Our approach also shows significant speedups compared with
PLASMA on both of these systems
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
Load Balancing Regular Meshes on SMPS with MPI
Domain decomposition for regular meshes on parallel computers has
traditionally been performed by attempting to exactly partition the work among the available processors (now cores). However, these
strategies often do not consider the inherent system noise which can hinder MPI application scalability to emerging peta-scale machines
with 10000+ nodes. In this work, we suggest a solution that uses a tunable hybrid static/dynamic scheduling strategy that can be incorporated into current MPI implementations of mesh codes. By applying this strategy to a 3D jacobi algorithm, we achieve performance gains
of at least 16% for 64 SMP nodes
MPI+X: task-based parallelization and dynamic load balance of finite element assembly
The main computing tasks of a finite element code(FE) for solving partial
differential equations (PDE's) are the algebraic system assembly and the
iterative solver. This work focuses on the first task, in the context of a
hybrid MPI+X paradigm. Although we will describe algorithms in the FE context,
a similar strategy can be straightforwardly applied to other discretization
methods, like the finite volume method. The matrix assembly consists of a loop
over the elements of the MPI partition to compute element matrices and
right-hand sides and their assemblies in the local system to each MPI
partition. In a MPI+X hybrid parallelism context, X has consisted traditionally
of loop parallelism using OpenMP. Several strategies have been proposed in the
literature to implement this loop parallelism, like coloring or substructuring
techniques to circumvent the race condition that appears when assembling the
element system into the local system. The main drawback of the first technique
is the decrease of the IPC due to bad spatial locality. The second technique
avoids this issue but requires extensive changes in the implementation, which
can be cumbersome when several element loops should be treated. We propose an
alternative, based on the task parallelism of the element loop using some
extensions to the OpenMP programming model. The taskification of the assembly
solves both aforementioned problems. In addition, dynamic load balance will be
applied using the DLB library, especially efficient in the presence of hybrid
meshes, where the relative costs of the different elements is impossible to
estimate a priori. This paper presents the proposed methodology, its
implementation and its validation through the solution of large computational
mechanics problems up to 16k cores
Invasive compute balancing for applications with shared and hybrid parallelization
This is the author manuscript. The final version is available from the publisher via the DOI in this record.Achieving high scalability with dynamically adaptive algorithms in high-performance computing (HPC) is a non-trivial task. The invasive paradigm using compute migration represents an efficient alternative to classical data migration approaches for such algorithms in HPC. We present a core-distribution scheduler which realizes the migration of computational power by distributing the cores depending on the requirements specified by one or more parallel program instances. We validate our approach with different benchmark suites for simulations with artificial workload as well as applications based on dynamically adaptive shallow water simulations, and investigate concurrently executed adaptivity parameter studies on realistic Tsunami simulations. The invasive approach results in significantly faster overall execution times and higher hardware utilization than alternative approaches. A dynamic resource management is therefore mandatory for a more efficient execution of scenarios similar to our simulations, e.g. several Tsunami simulations in urgent computing, to overcome strong scalability challenges in the area of HPC. The optimizations obtained by invasive migration of cores can be generalized to similar classes of algorithms with dynamic resource requirements.This work was supported by the German Research Foundation (DFG) as part
of the Transregional Collaborative Research Centre ”Invasive Computing”
(SFB/TR 89)
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