Refine
Year of publication
Document Type
- In Proceedings (43)
- Article (21)
- ZIB-Report (11)
- Book chapter (2)
- Other (2)
- In Collection (1)
- Poster (1)
Keywords
- GPGPU (2)
- Bioinformatics (1)
- C-H...O interaction (1)
- Concurrent Kernel Execution (1)
- Connected Component Labeling (1)
- Fault-tolerance (1)
- Hyper-Q (1)
- Ising Model (1)
- Oversubscription (1)
- Performance and usage measurement (1)
Institute
- Distributed Algorithms and Supercomputing (76)
- Modeling and Simulation of Complex Processes (3)
- ZIB Allgemein (3)
- Parallel and Distributed Computing (2)
- Visual Data Analysis (2)
- Visual and Data-centric Computing (2)
- Image Analysis in Biology and Materials Science (1)
- Numerical Mathematics (1)
- Supercomputing (1)
- Visual Data Analysis in Science and Engineering (1)
ZIB Structure Prediction Pipeline: Composing a Complex Biological Workflow Through Web Services
(2006)
\small Many interesting phenomena in molecular systems like interactions between macro-molecules, protein-substrate docking, or channeling processes in membranes are gouverned to a high degree by classical Coulomb or van-der-Waals forces. The visualization of these force fields is important for verifying numerical simulations. Moreover, by inspecting the forces visually we can gain deeper insight into the molecular processes. Up to now the visualization of vector fields is quite unusual in computational chemistry. In fact many commercial software packages do not support this topic at all. The reason is not that vector fields are considered unimportant, but mainly because of the lack of adequate visualization methods. In this paper we survey a number of methods for vector field visualization, ranging from well-known concepts like arrow or streamline plots to more advanced techniques like line integral convolution, and show how these can be applied to computational chemistry. A combination of the most meaningful methods in an interactive 3D visualization environment can provide a powerful tool box for analysing simulations in molecular dynamics.
THESEUS, the ZIB threading environment, is a parallel implementation of a protein threading based on a multi-queued branch-and-bound optimal search algorithm to find the best sequence-to-structure alignment through a library of template structures. THESEUS uses a template core model based on secondary structure definition and a scoring function based on knowledge-based potentials reflecting pairwise interactions and the chemical environment, as well as pseudo energies for homology detection, loop alignment, and secondary structure matching. The threading core is implemented in C++ as a SPMD parallization architecture using MPI for communication. The environment is designed for generic testing of different scoring functions, e.g. different secondary structure prediction terms, different scoring matrices and information derived from multiple sequence alignments. A validaton of the structure prediction results has been done on the basis of standard threading benchmark sets. THESEUS successfully participated in the 6th Critical Assessment of Techniques for Protein Structure Prediction (CASP) 2004.
With the growing number of hardware components and the increasing software complexity in the upcoming exascale computers, system failures will become the norm rather than an exception for long-running applications. Fault-tolerance can be achieved by the creation of checkpoints during the execution of a parallel program. Checkpoint/Restart (C/R) mechanisms allow for both task migration (even if there were no hardware faults) and restarting of tasks after the occurrence of hardware faults. Affected tasks are then migrated to other nodes which may result in unfortunate process placement and/or oversubscription of compute resources. In this paper we analyze the impact of unfortunate process placement and oversubscription of compute resources on the performance and scalability of two typical HPC application workloads, CP2K and MOM5. Results are given for a Cray XC30/40 with Aries dragonfly topology. Our results indicate that unfortunate process placement has only little negative impact while oversubscription substantially degrades the performance. The latter might be only (partially) beneficial when placing multiple applications with different computational characteristics on the same node.
Solving PDEs on unstructured grids is a cornerstone of engineering and scientific computing. Heterogeneous parallel platforms, including CPUs, GPUs, and FPGAs, enable energy-efficient and computationally demanding simulations.
In this article, we introduce the HPM C++-embedded DSL that bridges the abstraction gap between the mathematical formulation of mesh-based algorithms for PDE problems on the one hand and an increasing number of heterogeneous platforms with their different programming models on the other hand.
Thus, the HPM DSL aims at higher productivity in the code development process for multiple target platforms. We introduce the concepts as well as the basic structure of the HPM DSL, and demonstrate its usage with three examples. The mapping of the abstract algorithmic description onto parallel hardware, including distributed memory compute clusters, is presented.
A code generator and a matching back end allow the acceleration of HPM code with GPUs. Finally, the achievable performance and scalability are demonstrated for different example problems.
Simulations of the critical Ising model by means of local update algorithms suffer from critical slowing down. One way to partially compensate for the influence of this phenomenon on the runtime of simulations is using increasingly faster and parallel computer hardware. Another approach is using algorithms that do not suffer
from critical slowing down, such as cluster algorithms. This paper reports on the Swendsen-Wang multi-cluster algorithm on Intel Xeon Phi coprocessor 5110P, Nvidia Tesla M2090 GPU, and x86
multi-core CPU. We present shared memory versions of the said algorithm for the simulation of the two- and three-dimensional Ising model. We use a combination of local cluster search and global label reduction by means of atomic hardware primitives. Further, we
describe an MPI version of the algorithm on Xeon Phi and CPU, respectively. Significant performance improvements over known im
plementations of the Swendsen-Wang algorithm are demonstrated.
Solutions for biomedical grid computing – Case studies from the D-Grid project Services@MediGRID
(2011)
The computational power and availability of graphics processing units (GPUs), such as the Nvidia Tesla, and Many Integrated Core (MIC) processors, such as the Intel Xeon Phi, on high performance computing (HPC) systems is rapidly evolving. However, HPC applications need to be ported to take advantage of such hardware. This paper is a report on our experience of porting the MPI+OpenMP parallelised large-eddy simulation model (PALM) to multi-GPU as well as to MIC processor environments using the directive-based high level programming paradigm OpenACC and OpenMP, respectively. PALM is a Fortran-based computational fluid dynamics software package, used for the simulation of atmospheric and oceanic boundary layers to answer questions linked to fundamental atmospheric turbulence research, urban modelling, aircraft safety and cloud physics. Development of PALM started in 1997, the project currently entails 140 kLOC and is used on HPC farms of up to 43,200 cores. The main challenges we faced during the porting process are the size and complexity of the PALM code base, its inconsistent modularisation and the complete lack of a unit-test suite. We report the methods used to identify performance issues as well as our experiences with state-of-the-art profiling tools. Moreover, we outline the required porting steps in order to properly execute our code on GPUs and MIC processors, describe the problems and bottlenecks that we encountered during the porting process, and present separate performance tests for both architectures. These performance tests, however, do not provide any benchmark information that compares the performance of the ported code between the two architectures.
Recently, Intel released the oneAPI programming environment. With Data Parallel C++ (DPC++), oneAPI enables codes to target multiple hardware architectures like multi-core CPUs, GPUs, and even FPGAs or other hardware using a single source. For legacy codes that were written for Nvidia GPUs, a compatibility tool is provided which facilitates the transition to the SYCL-based DPC++ programming language. This paper presents early experiences when using both the compatibility tool and oneAPI as well the employed extension to the SYCL programming standard for the tsunami simulation code easyWave. A performance study compares the original code running on Xeon processors using OpenMP as well as CUDA with the performance of the DPC++ counter part on multicore CPUs as well as integrated GPUs.
Effective vectorization is becoming increasingly important for high performance and energy efficiency on processors with wide SIMD units. Compilers often require programmers to identify opportunities for vectorization, using directives to disprove data dependences. The OpenMP 4.x SIMD directives strive to provide portability. We investigate the ability of current compilers (GNU, Clang, and Intel) to generate SIMD code for microbenchmarks that cover common patterns in scientific codes and for two kernels from the VASP and the MOM5/ERGOM application. We explore coding strategies for improving SIMD performance across different compilers and platforms (Intel® Xeon® processor and Intel® Xeon Phi™ (co)processor). We compare OpenMP* 4.x SIMD vectorization with and without vector data types against SIMD intrinsics and C++ SIMD types. Our experiments show that in many cases portable performance can be achieved. All microbenchmarks are available as open source as a reference for programmers and compiler experts to enhance SIMD code generation.