Recent trends in the HPC field have introduced new CPU architectures with improved vectorization capabilities that require optimization to achieve peak performance and thus pose challenges for performance portability. The deployment of high-performing scientific applications for CPUs requires adapting the codebase and optimizing for performance. Evaluating these applications provides insights into the complex interactions between code, compilers, and hardware. We evaluate compiler auto-vectorization and explicit vectorization to achieve performance portability across modern CPUs with long vectors. We select a molecular docking application as a case study, as it represents computational patterns commonly found across HPC workloads. We report insights into the technical challenges, architectural trends, and optimization strategies relevant to the future development of scientific applications for HPC. Our results show which code transformations enable portable auto-vectorization, reaching performance similar to explicit vectorization. Experimental data confirms that x86 CPUs typically achieve higher execution performance than ARM CPUs, primarily due to their wider vectorization units. However, ARM architectures demonstrate competitive energy consumption and cost-effectiveness.

Drug discovery is a long and costly process aimed at finding a molecule that yields a therapeutic effect. Virtual screening is one of the initial in-silico steps that aims at estimating how promising a molecule is. This stage needs to solve two well-known domain problems: molecular docking and scoring. While the accuracy of scoring functions is extensively investigated in comparisons, the execution time of their implementation is usually not considered. In virtual screening campaigns, the definition of a fixed time budget for the entire process and the average time required to process each molecule determines the upper limit of the number of molecules that can be evaluated. By reducing the time needed to evaluate a single molecule, we can screen a larger number of molecules, thereby increasing the possibility of finding a promising solution. For extreme-scale virtual screening campaigns, the computational budget is a critical aspect since even utilizing large-scale facilities would make it impractical to complete the screening within a feasible time unless the computational time for a single molecule is significantly reduced. In this paper, we explore optimization and approximation techniques applied to two well-known scoring functions, which we modify to investigate different accuracy-performance trade-offs to support large-scale virtual screening campaigns. Despite the different approaches we considered, experimental results demonstrate that the proposed enhancements achieve better enrichment factors in virtual screening scenarios. Moreover, we port both implementations to CUDA to show that the proposed techniques are GPU-friendly and aligned with modern supercomputing infrastructures. • We optimized two SFs to explore the accuracy-throughput trade-off in extreme-scale VS. • We sped up X-SCORE 13 × via pre-computed approximations with a 10 % accuracy loss. • We sped up DrugScore ∼ 3 × via memoization, using saved time to improve scoring accuracy. • We ported SFs to CUDA to show how our methods are GPU-friendly and faster than on CPU. • We show that balancing accuracy-throughput can boost overall EF in large-scale VS.

In recent years, there has been a growing interest in developing high-performance implementations of drug discovery processing software. To target modern GPU architectures, such applications are mostly written in proprietary languages such as CUDA or HIP. However, with the increasing heterogeneity of modern HPC systems and the availability of accelerators from multiple hardware vendors, it has become critical to be able to efficiently execute drug discovery pipelines on multiple large-scale computing systems, with the ultimate goal of working on urgent computing scenarios. This article presents the challenges of migrating LiGen, an industrial drug discovery software pipeline, from CUDA to the SYCL programming model, an industry standard based on C++ that enables heterogeneous computing. We perform a structured analysis of the performance portability of the SYCL LiGen platform, focusing on different aspects of the approach from different perspectives. First, we analyze the performance portability provided by the high-level semantics of SYCL, including the most recent group algorithms and subgroups of SYCL 2020. Second, we analyze how low-level aspects such as kernel occupancy and register pressure affect the performance portability of the overall application. The experimental evaluation is performed on two different versions of LiGen, implementing two different parallelization patterns, by comparing them with a manually optimized CUDA version, and by evaluating performance portability using both known and ad hoc metrics. The results show that, thanks to the combination of high-level SYCL semantics and some manual tuning, LiGen achieves native-comparable performance on NVIDIA, while also running on AMD GPUs.

Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimised implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns.

Abstract Virtual screening is an early stage in the drug discovery process that selects the most promising candidates. In the urgent computing scenario, finding a solution in the shortest time frame is critical. Any improvement in the performance of a virtual screening application translates into an increase in the number of candidates evaluated, thereby raising the probability of finding a drug. In this paper, we show how we can improve application throughput using Out-of-kernel optimizations. They use input features, kernel requirements, and architectural features to rearrange the kernel inputs, executing them out of order, to improve the computation efficiency. These optimizations’ implementations are designed on an extreme-scale virtual screening application, named LiGen, that can hinge on CUDA and SYCL kernels to carry out the computation on modern supercomputer nodes. Even if they are tailored to a single application, they might also be of interest for applications that share a similar design pattern. The experimental results show how these optimizations can increase kernel performance by 2 $$\times$$ × , respectively, up to 2.2 $$\times$$ × in CUDA and up to 1.9 $$\times$$ × , in SYCL. Moreover, the reported speedup can be achieved with the best-proposed parameterization, as shown by the data we collected and reported in this manuscript.

Today digital revolution is having a dramatic impact on the pharmaceutical industry and the entire healthcare system. The implementation of machine learning, extreme-scale computer simulations, and big data analytics in the drug design and development process offers an excellent opportunity to lower the risk of investment and reduce the time to the patient.