24,232 research outputs found
Pipelining the Fast Multipole Method over a Runtime System
Fast Multipole Methods (FMM) are a fundamental operation for the simulation
of many physical problems. The high performance design of such methods usually
requires to carefully tune the algorithm for both the targeted physics and the
hardware. In this paper, we propose a new approach that achieves high
performance across architectures. Our method consists of expressing the FMM
algorithm as a task flow and employing a state-of-the-art runtime system,
StarPU, in order to process the tasks on the different processing units. We
carefully design the task flow, the mathematical operators, their Central
Processing Unit (CPU) and Graphics Processing Unit (GPU) implementations, as
well as scheduling schemes. We compute potentials and forces of 200 million
particles in 48.7 seconds on a homogeneous 160 cores SGI Altix UV 100 and of 38
million particles in 13.34 seconds on a heterogeneous 12 cores Intel Nehalem
processor enhanced with 3 Nvidia M2090 Fermi GPUs.Comment: No. RR-7981 (2012
A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
The overwhelmingly increasing amount of stored data has spurred researchers
seeking different methods in order to optimally take advantage of it which
mostly have faced a response time problem as a result of this enormous size of
data. Most of solutions have suggested materialization as a favourite solution.
However, such a solution cannot attain Real- Time answers anyhow. In this paper
we propose a framework illustrating the barriers and suggested solutions in the
way of achieving Real-Time OLAP answers that are significantly used in decision
support systems and data warehouses
Dynamic Graphs on the GPU
We present a fast dynamic graph data structure for the GPU. Our dynamic graph structure uses one hash table per vertex to store adjacency lists and achieves 3.4–14.8x faster insertion rates over the state of the art across a diverse set of large datasets, as well as deletion speedups up to 7.8x. The data structure supports queries and dynamic updates through both edge and vertex insertion and deletion. In addition, we define a comprehensive evaluation strategy based on operations, workloads, and applications that we believe better characterize and evaluate dynamic graph data structures
A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters
In this work, we consider the solution of boundary integral equations by
means of a scalable hierarchical matrix approach on clusters equipped with
graphics hardware, i.e. graphics processing units (GPUs). To this end, we
extend our existing single-GPU hierarchical matrix library hmglib such that it
is able to scale on many GPUs and such that it can be coupled to arbitrary
application codes. Using a model GPU implementation of a boundary element
method (BEM) solver, we are able to achieve more than 67 percent relative
parallel speed-up going from 128 to 1024 GPUs for a model geometry test case
with 1.5 million unknowns and a real-world geometry test case with almost 1.2
million unknowns. On 1024 GPUs of the cluster Titan, it takes less than 6
minutes to solve the 1.5 million unknowns problem, with 5.7 minutes for the
setup phase and 20 seconds for the iterative solver. To the best of the
authors' knowledge, we here discuss the first fully GPU-based
distributed-memory parallel hierarchical matrix Open Source library using the
traditional H-matrix format and adaptive cross approximation with an
application to BEM problems
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