TY - CHAP A1 - Wende, Florian A1 - Cordes, Frank A1 - Steinke, Thomas T1 - On Improving the Performance of Multi-threaded CUDA Applications with Concurrent Kernel Execution by Kernel Reordering T2 - Application Accelerators in High Performance Computing (SAAHPC), 2012 Symposium on Y1 - 2012 U6 - https://doi.org/10.1109/SAAHPC.2012.12 SP - 74 EP - 83 ER - TY - JOUR A1 - Cordes, Frank A1 - Preissner, Robert A1 - Steinke, Thomas T1 - How Does a Protein Work? JF - BioTOPics J. of Biotechnology Berlin-Brandenburg Y1 - 2003 VL - 18 IS - 2 SP - 4 EP - 5 ER - TY - CHAP A1 - Wende, Florian A1 - Cordes, Frank A1 - Steinke, Thomas T1 - Concurrent Kernel Execution on Xeon Phi within Parallel Heterogeneous Workloads T2 - Euro-Par 2014: Parallel Processing. 20th International Conference, Porto, Portugal, August 25-29, 2014, Proceedings Y1 - 2014 UR - http://www.springer.com/computer/swe/book/978-3-319-09872-2 U6 - https://doi.org/10.1007/978-3-319-09873-9_66 VL - 8632 SP - 788 EP - 799 ER - TY - CHAP A1 - Noack, Matthias A1 - Wende, Florian A1 - Steinke, Thomas A1 - Cordes, Frank T1 - A Unified Programming Model for Intra- and Inter-Node Offloading on Xeon Phi Clusters T2 - SC '14: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. SC14, November 16-21, 2014, New Orleans, Louisiana, USA N2 - Standard offload programming models for the Xeon Phi, e.g. Intel LEO and OpenMP 4.0, are restricted to a single compute node and hence a limited number of coprocessors. Scaling applications across a Xeon Phi cluster/supercomputer thus requires hybrid programming approaches, usually MPI+X. In this work, we present a framework based on heterogeneous active messages (HAM-Offload) that provides the means to offload work to local and remote (co)processors using a unified offload API. Since HAM-Offload provides similar primitives as current local offload frameworks, existing applications can be easily ported to overcome the single-node limitation while keeping the convenient offload programming model. We demonstrate the effectiveness of the framework by using it to enable a real-world application from the field of molecular dynamics to use multiple local and remote Xeon Phis. The evaluation shows good scaling behavior. Compared with LEO, performance is equal for large offloads and significantly better for small offloads. Y1 - 2014 UR - http://dl.acm.org/citation.cfm?id=2683616 U6 - https://doi.org/10.1109/SC.2014.22 ER - TY - GEN A1 - Wende, Florian A1 - Steinke, Thomas A1 - Cordes, Frank T1 - Multi-threaded Kernel Offloading to GPGPU Using Hyper-Q on Kepler Architecture N2 - Small-scale computations usually cannot fully utilize the compute capabilities of modern GPGPUs. With the Fermi GPU architecture Nvidia introduced the concurrent kernel execution feature allowing up to 16 GPU kernels to execute simultaneously on a shared GPU device for a better utilization of the respective resources. Insufficient scheduling capabilities in this respect, however, can significantly reduce the theoretical concurrency level. With the Kepler GPU architecture Nvidia addresses this issue by introducing the Hyper-Q feature with 32 hardware managed work queues for concurrent kernel execution. We investigate the Hyper-Q feature within heterogeneous workloads with multiple concurrent host threads or processes offloading computations to the GPU each. By means of a synthetic benchmark kernel and a hybrid parallel CPU-GPU real-world application, we evaluate the performance obtained with Hyper-Q on GPU and compare it against a kernel reordering mechanism introduced by the authors for the Fermi architecture. T3 - ZIB-Report - 14-19 KW - GPGPU KW - Hyper-Q KW - Concurrent Kernel Execution Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-50362 SN - 1438-0064 ER -