466 research outputs found
Iso-energy-efficiency: An approach to power-constrained parallel computation
Future large scale high performance supercomputer systems require high energy efficiency to achieve exaflops computational power and beyond. Despite the need to understand energy efficiency in high-performance systems, there are few techniques to evaluate energy efficiency at scale. In this paper, we propose a system-level iso-energy-efficiency model to analyze, evaluate and predict energy-performance of data intensive parallel applications with various execution patterns running on large scale power-aware clusters. Our analytical model can help users explore the effects of machine and application dependent characteristics on system energy efficiency and isolate efficient ways to scale system parameters (e.g. processor count, CPU power/frequency, workload size and network bandwidth) to balance energy use and performance. We derive our iso-energy-efficiency model and apply it to the NAS Parallel Benchmarks on two power-aware clusters. Our results indicate that the model accurately predicts total system energy consumption within 5% error on average for parallel applications with various execution and communication patterns. We demonstrate effective use of the model for various application contexts and in scalability decision-making
On the acceleration of wavefront applications using distributed many-core architectures
In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applicationsâa ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P). Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures
A Flexible Patch-Based Lattice Boltzmann Parallelization Approach for Heterogeneous GPU-CPU Clusters
Sustaining a large fraction of single GPU performance in parallel
computations is considered to be the major problem of GPU-based clusters. In
this article, this topic is addressed in the context of a lattice Boltzmann
flow solver that is integrated in the WaLBerla software framework. We propose a
multi-GPU implementation using a block-structured MPI parallelization, suitable
for load balancing and heterogeneous computations on CPUs and GPUs. The
overhead required for multi-GPU simulations is discussed in detail and it is
demonstrated that the kernel performance can be sustained to a large extent.
With our GPU implementation, we achieve nearly perfect weak scalability on
InfiniBand clusters. However, in strong scaling scenarios multi-GPUs make less
efficient use of the hardware than IBM BG/P and x86 clusters. Hence, a cost
analysis must determine the best course of action for a particular simulation
task. Additionally, weak scaling results of heterogeneous simulations conducted
on CPUs and GPUs simultaneously are presented using clusters equipped with
varying node configurations.Comment: 20 pages, 12 figure
The End of Slow Networks: It's Time for a Redesign
Next generation high-performance RDMA-capable networks will require a
fundamental rethinking of the design and architecture of modern distributed
DBMSs. These systems are commonly designed and optimized under the assumption
that the network is the bottleneck: the network is slow and "thin", and thus
needs to be avoided as much as possible. Yet this assumption no longer holds
true. With InfiniBand FDR 4x, the bandwidth available to transfer data across
network is in the same ballpark as the bandwidth of one memory channel, and it
increases even further with the most recent EDR standard. Moreover, with the
increasing advances of RDMA, the latency improves similarly fast. In this
paper, we first argue that the "old" distributed database design is not capable
of taking full advantage of the network. Second, we propose architectural
redesigns for OLTP, OLAP and advanced analytical frameworks to take better
advantage of the improved bandwidth, latency and RDMA capabilities. Finally,
for each of the workload categories, we show that remarkable performance
improvements can be achieved
Realfast: Real-Time, Commensal Fast Transient Surveys with the Very Large Array
Radio interferometers have the ability to precisely localize and better
characterize the properties of sources. This ability is having a powerful
impact on the study of fast radio transients, where a few milliseconds of data
is enough to pinpoint a source at cosmological distances. However, recording
interferometric data at millisecond cadence produces a terabyte-per-hour data
stream that strains networks, computing systems, and archives. This challenge
mirrors that of other domains of science, where the science scope is limited by
the computational architecture as much as the physical processes at play. Here,
we present a solution to this problem in the context of radio transients:
realfast, a commensal, fast transient search system at the Jansky Very Large
Array. Realfast uses a novel architecture to distribute fast-sampled
interferometric data to a 32-node, 64-GPU cluster for real-time imaging and
transient detection. By detecting transients in situ, we can trigger the
recording of data for those rare, brief instants when the event occurs and
reduce the recorded data volume by a factor of 1000. This makes it possible to
commensally search a data stream that would otherwise be impossible to record.
This system will search for millisecond transients in more than 1000 hours of
data per year, potentially localizing several Fast Radio Bursts, pulsars, and
other sources of impulsive radio emission. We describe the science scope for
realfast, the system design, expected outcomes, and ways real-time analysis can
help in other fields of astrophysics.Comment: Accepted to ApJS Special Issue on Data; 11 pages, 4 figure
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