Cloud-native virtualization technology combines virtualization technology with cloud-native computing to provide a more efficient, flexible, and scalable cloud computing environment. In the process of analysis and research in the field of bioinformatics, it is usually necessary to deal with large-scale data sets and complex computing tasks, and the demand for computing power throughout the research and development cycle is characterized by peaks and troughs. The elastic scalability of cloud-native virtualization technology allows for the expansion of computing resources according to demand, meeting the data processing and analysis requirements throughout the entire research and development cycle. By integrating virtualized InfiniBand high-speed NICs, data transfer and the execution of computational tasks are accelerated, further reducing the research and development cycle. In summary, cloud-native virtualization technology has significant application value in the field of bioinformatics, providing an efficient computing environment while saving time and costs.
Vienna ab initio Simulation Package(VASP) is software for performing electronic structure calculations and quantum mechanical molecular dynamics simulations, and is widely used in materials simulation and computational material science research. Currently VASP is accelerated on NVIDIA GPUs via the OpenACC programming model, which cannot be directly compiled and used by domestic GPU like accelerator platform. We use the HIP programming model to port VASP to GPU like accelerator. Refer to the OpenACC port of VASP, we use the HIP API for device and data management, write kernel to implement cyclic accelerated computation, and use mathematical libraries such as hipBLAS and hipFFT to support the computation. After the porting was completed we compare the computed results of the HIP port version of VASP with the CPU version and the OpenACC version. We passed the validation of the correctness of the HIP ported version. We also analyze the principle of All-to-All parallel communication and propose optimization strategies such as aggregated communication and merged copy for the case of multiple bands communication, which are implemented on the HIP port version. We choose the B.hR105 testcase to test the All-to-All before and after optimization. The results demonstrate a 25.57% performance improvement after optimization.
VASP is a widely used software, performing electronic structure calculations and quantum mechanical molecular dynamics from first principle, in the areas of material modeling and computational material science. To obtain better computing performance, VASP usually use heterogeneous architectures for acceleration. VASP6 is currently accelerated on NVIDIA GPUs with OpenACC programming model, which cannot be compiled and run on domestic heterogeneous platform. In order to use VASP on domestic heterogeneous platforms, it is necessary to design a new transplantation strategy. Following the OpenACC GPU-port, VASP was ported on the domestic heterogeneous platform with the HIP- C/C++ programming model and libraries on the platform. Hybrid density functional calculation is a hotspot of calculation in VASP. This calculation is frequently used but takes a long time. In order to provide good calculation services for researchers, it needs to be optimized. For a typical hybrid functional calculation example, this paper uses the performance analysis tool hipprof to find out performance bottlenecks. Combined with the characteristics of GPU like accelerator hardware architecture, multi-stream concurrency optimization and hardware resource allocation optimization are performed on the compute-intensive exact exchange computation. The test results shows that the calculation performance of the exact exchange is improved by 17.19% compared with the initial porting, which proves the effectiveness of the optimization.
In scientific and engineering computations, it is often necessary to solve eigenvalue problems of symmetric matrices. With the rapid development of high-performance computing, the adaptation and optimization of linear algebra solution methods, including eigenvalue problem solving, on heterogeneous platforms have become increasingly important. In this paper, we propose a divide-and-conquer method for solving symmetric matrix eigenvalue problems, which is implemented based on the SOLVER library of domestic heterogeneous platforms using the HIP programming model. We take full advantage of the multicore strengths of domestic accelerators and parallelize the solution process, such as the secular equation. Parallel reduction and merged computation optimization techniques are employed to further improve performance. Compared with implementations such as MAGMA, our optimized interface demonstrates good stability and high accuracy in various scale test cases of real-world applications, as well as a significant performance advantage. The results show that in large-scale matrix eigenvalue problems, the performance is more than twice that of the related interfaces in the MAGMA library.
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