Model-Based Optimization of EULAG Kernel on Intel Xeon Phi Through Load Imbalancing

Lastovetsky, Alexey; Szustak, Łukasz; Wyrzykowski, Roman

doi:10.1109/tpds.2016.2599527

Cited by 37 publications

(39 citation statements)

References 36 publications

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“…22,23 It should be noted that the speeds and the energies that we refer to here are actually sample means output by a statistical method. All the processors take part in the execution of this function.…”

Section: Adaptaleph: Self-adaptable Data Partitioning Algorithm Solvimentioning

confidence: 99%

Design of self‐adaptable data parallel applications on multicore clusters automatically optimized for performance and energy through load distribution

Manumachu

Lastovetsky

2018

Concurrency and Computation

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Self-adaptability is a highly preferred feature in HPC applications. A crucial building block of a self-adaptable application is a data partitioning algorithm that must possess several essential qualities apart from low runtime and memory costs. On modern platforms composed of multicore CPU processors, data partitioning algorithms striving to solve the bi-objective optimization problem for performance and energy (BOPPE) face a formidable challenge. They must take into account the new complexities inherent in these platforms such as severe resource contention and non-uniform memory access (NUMA). Novel model-based methods and data partitioning algorithms have been proposed that address the challenge. However, these methods take as input full functional performance and energy models (FPM and FEM), which have prohibitively high model construction costs. Therefore, they are not suitable for employment in self-adaptable applications. In this paper, we present a self-adaptable data partitioning algorithm called ADAPTALEPH, which solves BOPPE on homogeneous clusters of multicore CPUs. Unlike the state-of-the-art solving BOPPE that take as inputs full FPM and FEM, it constructs partial FPM and FEM during its execution using all the available processors. It returns a locally Pareto-optimal set of solutions, which are the heterogeneous workload distributions that achieve inter-node optimization of data-parallel applications for performance and energy. We experimentally study the efficiency of ADAPTALEPH for three data-parallel applications, ie, matrix-vector multiplication, matrix-matrix multiplication, and fast Fourier transform, on a modern multicore CPU and simulations for homogeneous clusters of such CPUs. We demonstrate that the locally Pareto-optimal front approaches the globally Pareto-optimal front as the number of points in the partial discrete FPM and FEM functions are increased. The number of points in the partial FPM/FEM when the locally Pareto-optimal front becomes the globally Pareto-optimal front is considerably less than the number of points in the full FPM/FEM thereby suggesting development of methods that can leverage this finding to drastically reduce the model construction times.

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Section: Adaptaleph: Self-adaptable Data Partitioning Algorithm Solvimentioning

confidence: 99%

Design of self‐adaptable data parallel applications on multicore clusters automatically optimized for performance and energy through load distribution

Manumachu

Lastovetsky

2018

Concurrency and Computation

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“…In our previous works [7,14,15], we proposed methods for the adaptation of MPDATA to multi-/manycore systems. These methods contribute to ease memory and communication bounds and to better exploit computation resources of sharedmemory systems, including CPUs and Intel Xeon Phi.…”

Section: Adaptation Of Data-flow Synchronization Strategy To Mpdatamentioning

confidence: 99%

“…Reaching this goal requires to develop more effi-cient programming abstractions enabling better utilization of computing resources [7]. In particular, the still growing parallelism of emerging computing systems makes the synchronization issues of critical importance for scaling application performance on these systems [4].…”

Section: Introductionmentioning

confidence: 99%

Strategy for data-flow synchronizations in stencil parallel computations on multi-/manycore systems

Szustak

2018

J Supercomput

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In this paper, an innovative strategy for the data-flow synchronization in shared-memory systems is proposed. This strategy assumes to synchronize only interdependent threads instead of using the barrier approach that-in contrast to our approach-synchronize all threads. We demonstrate the adaptation of the data-flow synchronization strategy to two complex scientific applications based on stencil codes. An algorithm for the data-flow synchronization is developed and successfully used for both applications. The proposed approach is evaluated for various Intel microarchitectures released in the last 5 years, including the newest processors: Skylake and Knights Landing. The important part of this assessment is the performance comparison of the proposed data-flow synchronization with the OpenMP barrier. The experimental results show that the performance of the studied applications can be accelerated up to 1.3 times using the proposed data-flow synchronizations strategy.

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“…Therefore, we believe that it may also be present in many other so‐called regular applications. Another recent example is the MPDATA code, part of the EULAG (Eulerian/semi‐Lagrangian fluid solver), which has been shown to suffer from heavy spatial imbalance when executed on a recent Xeon Phi architecture despite its strong regularity.…”

Section: Ondes3d: a Typical Imbalanced Mpi Codementioning

confidence: 99%

Performance modeling of a geophysics application to accelerate over‐decomposition parameter tuning through simulation

Tesser

Schnorr

Legrand

et al. 2018

Concurrency and Computation

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Summary Finite‐difference methods are commonplace in High Performance Computing applications. Despite their apparent regularity, they often exhibit load imbalance that damages their efficiency. We characterize the spatial and temporal load imbalance of Ondes3D, a typical finite‐differences application dedicated to earthquake modeling. Our analysis reveals imbalance originating from the structure of the input data, and from low‐level CPU optimizations. Ondes3D was successfully ported to AMPI/CHARM++ using over‐decomposition and MPI process migration techniques to dynamically rebalance the load. However, this approach requires careful selection of the over‐decomposition level, the load balancing algorithm, and its activation frequency. These choices are usually tied to application structure and platform characteristics. In this article, we propose a workflow that leverages the capabilities of SimGrid to conduct such study at low experimental cost. We rely on a combination of emulation, simulation, and application modeling that requires minimal code modification and manages to capture both spatial and temporal load imbalance to faithfully predict the performance of dynamic load balancing. We evaluate the quality of our simulation by comparing simulation results with the outcome of real executions and demonstrate how this approach can be used to quickly find the optimal load balancing configuration for a given application/hardware configuration.

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Model-Based Optimization of EULAG Kernel on Intel Xeon Phi Through Load Imbalancing

Cited by 37 publications

References 36 publications

Design of self‐adaptable data parallel applications on multicore clusters automatically optimized for performance and energy through load distribution

Design of self‐adaptable data parallel applications on multicore clusters automatically optimized for performance and energy through load distribution

Strategy for data-flow synchronizations in stencil parallel computations on multi-/manycore systems

Performance modeling of a geophysics application to accelerate over‐decomposition parameter tuning through simulation

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