arXiv subjects

James Southern

Publications and source records attributed to James Southern.

6 recordsLinked to original sources

arXiv2026

SPEC CPU: The Next Generation

The march toward developing relevant and robust CPU benchmarks continues with the introduction of SPEC CPU 2026, the next generation suite for measuring processor performance. This paper details the methodology behind its creation, showcasing a process centered on community collaboration and principled development. The suite is built upon a foundation of modern, open-source applications, selected and hardened through a process that emphasizes workload diversity, portability, and software longevity. A key contribution is Rolling-Round-Robin Rate, a novel and standardized approach to running heterogeneous, multiprogrammed workloads that addresses a long-standing gap in benchmarking practice. Additionally, the suite features an expanded set of multithreaded benchmarks and introduces workloads with distinct microarchitectural profiles, reflecting the demands of contemporary software. By detailing our principled approach to benchmark selection, adaptation, and validation, we demonstrate how the SPEC CPU 2026 suite sets the standard for performance evaluation in the next era of computer architecture research and development.

cs.PF↗

arXiv2014

Scalable and Fault Tolerant Computation with the Sparse Grid Combination Technique

This paper continues to develop a fault tolerant extension of the sparse grid combination technique recently proposed in [B. Harding and M. Hegland, ANZIAM J., 54 (CTAC2012), pp. C394-C411]. The approach is novel for two reasons, first it provides several levels in which one can exploit parallelism leading towards massively parallel implementations, and second, it provides algorithm-based fault tolerance so that solutions can still be recovered if failures occur during computation. We present a generalisation of the combination technique from which the fault tolerant algorithm is a consequence. Using a model for the time between faults on each node of a high performance computer we provide bounds on the expected error for interpolation with this algorithm. Numerical experiments on the scalar advection PDE demonstrate that the algorithm is resilient to faults on a real application. It is observed that the trade-off of recovery time to decreased accuracy of the solution is suitably small. A comparison with traditional checkpoint-restart methods applied to the combination technique show that our approach is highly scalable with respect to the number of faults.

math.NA↗

arXiv2013

A thread-parallel algorithm for anisotropic mesh adaptation

Anisotropic mesh adaptation is a powerful way to directly minimise the computational cost of mesh based simulation. It is particularly important for multi-scale problems where the required number of floating-point operations can be reduced by orders of magnitude relative to more traditional static mesh approaches. Increasingly, finite element and finite volume codes are being optimised for modern multi-core architectures. Typically, decomposition methods implemented through the Message Passing Interface (MPI) are applied for inter-node parallelisation, while a threaded programming model, such as OpenMP, is used for intra-node parallelisation. Inter-node parallelism for mesh adaptivity has been successfully implemented by a number of groups. However, thread-level parallelism is significantly more challenging because the underlying data structures are extensively modified during mesh adaptation and a greater degree of parallelism must be realised. In this paper we describe a new thread-parallel algorithm for anisotropic mesh adaptation algorithms. For each of the mesh optimisation phases (refinement, coarsening, swapping and smoothing) we describe how independent sets of tasks are defined. We show how a deferred updates strategy can be used to update the mesh data structures in parallel and without data contention. We show that despite the complex nature of mesh adaptation and inherent load imbalances in the mesh adaptivity, a parallel efficiency of 60% is achieved on an 8 core Intel Xeon Sandybridge, and a 40% parallel efficiency is achieved using 16 cores in a 2 socket Intel Xeon Sandybridge ccNUMA system.

cs.DC↗

arXiv2013

Benchmarking mixed-mode PETSc performance on high-performance architectures

The trend towards highly parallel multi-processing is ubiquitous in all modern computer architectures, ranging from handheld devices to large-scale HPC systems; yet many applications are struggling to fully utilise the multiple levels of parallelism exposed in modern high-performance platforms. In order to realise the full potential of recent hardware advances, a mixed-mode between shared-memory programming techniques and inter-node message passing can be adopted which provides high-levels of parallelism with minimal overheads. For scientific applications this entails that not only the simulation code itself, but the whole software stack needs to evolve. In this paper, we evaluate the mixed-mode performance of PETSc, a widely used scientific library for the scalable solution of partial differential equations. We describe the addition of OpenMP threaded functionality to the library, focusing on sparse matrix-vector multiplication. We highlight key challenges in achieving good parallel performance, such as explicit communication overlap using task-based parallelism, and show how to further improve performance by explicitly load balancing threads within MPI processes. Using a set of matrices extracted from Fluidity, a CFD application code which uses the library as its linear solver engine, we then benchmark the parallel performance of mixed-mode PETSc across multiple nodes on several modern HPC architectures. We evaluate the parallel scalability on Uniform Memory Access (UMA) systems, such as the Fujitsu PRIMEHPC FX10 and IBM BlueGene/Q, as well as a Non-Uniform Memory Access (NUMA) Cray XE6 platform. A detailed comparison is performed which highlights the characteristics of each particular architecture, before demonstrating efficient strong scalability of sparse matrix-vector multiplication with significant speedups over the pure-MPI mode.

cs.DC↗

arXiv2013

Achieving Efficient Strong Scaling with PETSc using Hybrid MPI/OpenMP Optimisation

The increasing number of processing elements and decreas- ing memory to core ratio in modern high-performance platforms makes efficient strong scaling a key requirement for numerical algorithms. In order to achieve efficient scalability on massively parallel systems scientific software must evolve across the entire stack to exploit the multiple levels of parallelism exposed in modern architectures. In this paper we demonstrate the use of hybrid MPI/OpenMP parallelisation to optimise parallel sparse matrix-vector multiplication in PETSc, a widely used scientific library for the scalable solution of partial differential equations. Using large matrices generated by Fluidity, an open source CFD application code which uses PETSc as its linear solver engine, we evaluate the effect of explicit communication overlap using task-based parallelism and show how to further improve performance by explicitly load balancing threads within MPI processes. We demonstrate a significant speedup over the pure-MPI mode and efficient strong scaling of sparse matrix-vector multiplication on Fujitsu PRIMEHPC FX10 and Cray XE6 systems.

cs.DC↗

arXiv2012

Mixed-mode implementation of PETSc for scalable linear algebra on multi-core processors

With multi-core processors a ubiquitous building block of modern supercomputers, it is now past time to enable applications to embrace these developments in processor design. To achieve exascale performance, applications will need ways of exploiting the new levels of parallelism that are exposed in modern high-performance computers. A typical approach to this is to use shared-memory programming techniques to best exploit multi-core nodes along with inter-node message passing. In this paper, we describe the addition of OpenMP threaded functionality to the PETSc library. We highlight some issues that hinder good performance of threaded applications on modern processors and describe how to negate them. The OpenMP branch of PETSc was benchmarked using matrices extracted from Fluidity, a CFD application code, which uses the library as its linear solver engine. The overall performance of the mixed-mode implementation is shown to be superior to that of the pure-MPI version.

cs.DC↗