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Preparing sparse solvers for exascale computing.
Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'
Havens: Explicit Reliable Memory Regions for HPC Applications
Supporting error resilience in future exascale-class supercomputing systems
is a critical challenge. Due to transistor scaling trends and increasing memory
density, scientific simulations are expected to experience more interruptions
caused by transient errors in the system memory. Existing hardware-based
detection and recovery techniques will be inadequate to manage the presence of
high memory fault rates.
In this paper we propose a partial memory protection scheme based on
region-based memory management. We define the concept of regions called havens
that provide fault protection for program objects. We provide reliability for
the regions through a software-based parity protection mechanism. Our approach
enables critical program objects to be placed in these havens. The fault
coverage provided by our approach is application agnostic, unlike
algorithm-based fault tolerance techniques.Comment: 2016 IEEE High Performance Extreme Computing Conference (HPEC '16),
September 2016, Waltham, MA, US
Towards Exascale Scientific Metadata Management
Advances in technology and computing hardware are enabling scientists from
all areas of science to produce massive amounts of data using large-scale
simulations or observational facilities. In this era of data deluge, effective
coordination between the data production and the analysis phases hinges on the
availability of metadata that describe the scientific datasets. Existing
workflow engines have been capturing a limited form of metadata to provide
provenance information about the identity and lineage of the data. However,
much of the data produced by simulations, experiments, and analyses still need
to be annotated manually in an ad hoc manner by domain scientists. Systematic
and transparent acquisition of rich metadata becomes a crucial prerequisite to
sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and
domain-agnostic metadata management infrastructure that can meet the demands of
extreme-scale science is notable by its absence.
To address this gap in scientific data management research and practice, we
present our vision for an integrated approach that (1) automatically captures
and manipulates information-rich metadata while the data is being produced or
analyzed and (2) stores metadata within each dataset to permeate
metadata-oblivious processes and to query metadata through established and
standardized data access interfaces. We motivate the need for the proposed
integrated approach using applications from plasma physics, climate modeling
and neuroscience, and then discuss research challenges and possible solutions
Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond
In this and a set of companion whitepapers, the USQCD Collaboration lays out
a program of science and computing for lattice gauge theory. These whitepapers
describe how calculation using lattice QCD (and other gauge theories) can aid
the interpretation of ongoing and upcoming experiments in particle and nuclear
physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers
Preparing HPC Applications for the Exascale Era: A Decoupling Strategy
Production-quality parallel applications are often a mixture of diverse
operations, such as computation- and communication-intensive, regular and
irregular, tightly coupled and loosely linked operations. In conventional
construction of parallel applications, each process performs all the
operations, which might result inefficient and seriously limit scalability,
especially at large scale. We propose a decoupling strategy to improve the
scalability of applications running on large-scale systems.
Our strategy separates application operations onto groups of processes and
enables a dataflow processing paradigm among the groups. This mechanism is
effective in reducing the impact of load imbalance and increases the parallel
efficiency by pipelining multiple operations. We provide a proof-of-concept
implementation using MPI, the de-facto programming system on current
supercomputers. We demonstrate the effectiveness of this strategy by decoupling
the reduce, particle communication, halo exchange and I/O operations in a set
of scientific and data-analytics applications. A performance evaluation on
8,192 processes of a Cray XC40 supercomputer shows that the proposed approach
can achieve up to 4x performance improvement.Comment: The 46th International Conference on Parallel Processing (ICPP-2017
Multi-GPU Acceleration of the iPIC3D Implicit Particle-in-Cell Code
iPIC3D is a widely used massively parallel Particle-in-Cell code for the
simulation of space plasmas. However, its current implementation does not
support execution on multiple GPUs. In this paper, we describe the porting of
iPIC3D particle mover to GPUs and the optimization steps to increase the
performance and parallel scaling on multiple GPUs. We analyze the strong
scaling of the mover on two GPU clusters and evaluate its performance and
acceleration. The optimized GPU version which uses pinned memory and
asynchronous data prefetching outperform their corresponding CPU versions by
5-10x on two different systems equipped with NVIDIA K80 and V100 GPUs.Comment: Accepted for publication in ICCS 201
Evaluating the Impact of SDC on the GMRES Iterative Solver
Increasing parallelism and transistor density, along with increasingly
tighter energy and peak power constraints, may force exposure of occasionally
incorrect computation or storage to application codes. Silent data corruption
(SDC) will likely be infrequent, yet one SDC suffices to make numerical
algorithms like iterative linear solvers cease progress towards the correct
answer. Thus, we focus on resilience of the iterative linear solver GMRES to a
single transient SDC. We derive inexpensive checks to detect the effects of an
SDC in GMRES that work for a more general SDC model than presuming a bit flip.
Our experiments show that when GMRES is used as the inner solver of an
inner-outer iteration, it can "run through" SDC of almost any magnitude in the
computationally intensive orthogonalization phase. That is, it gets the right
answer using faulty data without any required roll back. Those SDCs which it
cannot run through, get caught by our detection scheme
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