Portability and Scalability of OpenMP Offloading on State-of-the-art Accelerators

Over the last decade, most of the increase in computing power has been gained by advances in accelerated many-core architectures, mainly in the form of GPGPUs. While accelerators achieve phenomenal performances in various computing tasks, their utilization requires code adaptations and transformations. Thus, OpenMP, the most common standard for multi-threading in scientific computing applications, introduced offloading capabilities between host (CPUs) and accelerators since v4.0, with increasing support in the successive v4.5, v5.0, v5.1, and the latest v5.2 versions. Recently, two state-of-the-art GPUs - the Intel Ponte Vecchio Max 1100 and the NVIDIA A100 GPUs - were released to the market, with the oneAPI and GNU LLVM-backed compilation for offloading, correspondingly. In this work, we present early performance results of OpenMP offloading capabilities to these devices while specifically analyzing the potability of advanced directives (using SOLLVE's OMPVV test suite) and the scalability of the hardware in representative scientific mini-app (the LULESH benchmark). Our results show that the vast majority of the offloading directives in v4.5 and 5.0 are supported in the latest oneAPI and GNU compilers; however, the support in v5.1 and v5.2 is still lacking. From the performance perspective, we found that PVC is up to 37% better than the A100 on the LULESH benchmark, presenting better performance in computing and data movements.Comment: 13 page

Progress towards accelerating the unified model on hybrid multi-core systems

he cloud microphysics scheme, CASIM, and the radiation scheme, SOCRATES, are two computationally intensive parts within the Met Office’s Unified Model (UM). This study enables CASIM and SOCRATES to use accelerated multi-core systems for optimal com- putational performance of the UM. Using profiling to guide our efforts, we refactored the code for optimal threading and kernel arrangement and implemented OpenACC directives manually or through the CLAW source-to-source translator. Initial porting re- sults achieved 10.02x and 9.25x speedup in CASIM and SOCRATES respectively on 1 GPU compared with 1 CPU core. A granular per- formance analysis of the strategy and bottlenecks are discussed. These improvements will enable UM to run on heterogeneous com- puters and a path forward for further improvements is provided

Contributions to the efficient use of general purpose coprocessors: kernel density estimation as case study

142 p.The high performance computing landscape is shifting from assemblies of homogeneous nodes towards heterogeneous systems, in which nodes consist of a combination of traditional out-of-order execution cores and accelerator devices. Accelerators provide greater theoretical performance compared to traditional multi-core CPUs, but exploiting their computing power remains as a challenging task.This dissertation discusses the issues that arise when trying to efficiently use general purpose accelerators. As a contribution to aid in this task, we present a thorough survey of performance modeling techniques and tools for general purpose coprocessors. Then we use as case study the statistical technique Kernel Density Estimation (KDE). KDE is a memory bound application that poses several challenges for its adaptation to the accelerator-based model. We present a novel algorithm for the computation of KDE that reduces considerably its computational complexity, called S-KDE. Furthermore, we have carried out two parallel implementations of S-KDE, one for multi and many-core processors, and another one for accelerators. The latter has been implemented in OpenCL in order to make it portable across a wide range of devices. We have evaluated the performance of each implementation of S-KDE in a variety of architectures, trying to highlight the bottlenecks and the limits that the code reaches in each device. Finally, we present an application of our S-KDE algorithm in the field of climatology: a novel methodology for the evaluation of environmental models

Doctor of Philosophy

dissertationHigh Performance Computing (HPC) on-node parallelism is of extreme importance to guarantee and maintain scalability across large clusters of hundreds of thousands of multicore nodes. HPC programming is dominated by the hybrid model "MPI + X", with MPI to exploit the parallelism across the nodes, and "X" as some shared memory parallel programming model to accomplish multicore parallelism across CPUs or GPUs. OpenMP has become the "X" standard de-facto in HPC to exploit the multicore architectures of modern CPUs. Data races are one of the most common and insidious of concurrent errors in shared memory programming models and OpenMP programs are not immune to them. The OpenMP-provided ease of use to parallelizing programs can often make it error-prone to data races which become hard to find in large applications with thousands lines of code. Unfortunately, prior tools are unable to impact practice owing to their poor coverage or poor scalability. In this work, we develop several new approaches for low overhead data race detection. Our approaches aim to guarantee high precision and accuracy of race checking while maintaining a low runtime and memory overhead. We present two race checkers for C/C++ OpenMP programs that target two different classes of programs. The first, ARCHER, is fast but requires large amount of memory, so it ideally targets applications that require only a small portion of the available on-node memory. On the other hand, SWORD strikes a balance between fast zero memory overhead data collection followed by offline analysis that can take a long time, but it often report most races quickly. Given that race checking was impossible for large OpenMP applications, our contributions are the best available advances in what is known to be a difficult NP-complete problem. We performed an extensive evaluation of the tools on existing OpenMP programs and HPC benchmarks. Results show that both tools guarantee to identify all the races of a program in a given run without reporting any false alarms. The tools are user-friendly, hence serve as an important instrument for the daily work of programmers to help them identify data races early during development and production testing. Furthermore, our demonstrated success on real-world applications puts these tools on the top list of debugging tools for scientists at large

Efficient algorithms for the fast computation of space charge effects caused by charged particles in particle accelerators

In this dissertation, a Poisson solver is improved with three parts: the efficient integrated Green's function; the discrete cosine transform of the efficient integrated Green's function values; the implicitly zero-padded fast Fourier transform for charge density. In addition, the high performance computing technology is utilized for the further improvement of efficiency, such as: OpenMP API, OpenMP+CUDA, MPI, and MPI+OpenMP parallelizations. The examples and simulation results are matched with the results of the commonly used Poisson solver to demonstrate the accuracy performance