Over the past years GPUs have been successfully applied to the task of inverting the fermion matrix in lattice QCD calculations. Even strong scaling to capability-level supercomputers, corresponding to O(100) GPUs or more has been achieved. However s
trong scaling a whole gauge field generation algorithm to this regim requires significantly more functionality than just having the matrix inverter utilizing the GPUs and has not yet been accomplished. This contribution extends QDP-JIT, the migration of SciDAC QDP++ to GPU-enabled parallel systems, to help to strong scale the whole Hybrid Monte-Carlo to this regime. Initial results are shown for gauge field generation with Chroma simulating pure Wilson fermions on OLCF TitanDev.
Extensions to the C++ implementation of the QCD Data Parallel Interface are provided enabling acceleration of expression evaluation on NVIDIA GPUs. Single expressions are off-loaded to the device memory and execution domain leveraging the Portable Ex
pression Template Engine and using Just-in-Time compilation techniques. Memory management is automated by a software implementation of a cache controlling the GPUs memory. Interoperability with existing Krylov space solvers is demonstrated and special attention is paid on Chroma readiness. Non-kernel routines in lattice QCD calculations typically not subject of hand-tuned optimisations are accelerated which can reduce the effects otherwise suffered from Amdahls Law.
Graphic Processing Units (GPUs) are getting increasingly important as target architectures in scientific High Performance Computing (HPC). NVIDIA established CUDA as a parallel computing architecture controlling and making use of the compute power of
GPUs. CUDA provides sufficient support for C++ language elements to enable the Expression Template (ET) technique in the device memory domain. QDP++ is a C++ vector class library suited for quantum field theory which provides vector data types and expressions and forms the basis of the lattice QCD software suite Chroma. In this work accelerating QDP++ expression evaluation to a GPU was successfully implemented leveraging the ET technique and using Just-In-Time (JIT) compilation. The Portable Expression Template Engine (PETE) and the C API for CUDA kernel arguments were used to build the bridge between host and device memory domains. This provides the possibility to accelerate Chroma routines to a GPU which are typically not subject to special optimisation. As an application example a smearing routine was accelerated to execute on a GPU. A significant speed-up compared to normal CPU execution could be measured.
