Aims
DWP16001 is a novel sodium–glucose cotransporter 2 inhibitor for the treatment of type 2 diabetes with selective and sustained sodium–glucose cotransporter 2 inhibition. We aimed to evaluate ...whether the coadministration of DWP16001 and metformin causes any changes in pharmacokinetics (PK) or pharmacodynamics (PD).
Methods
A randomized, open‐label, single‐ and multiple‐dose, 2‐sequence, crossover study was conducted in healthy male subjects. Subjects received the following treatments: a single oral dose of DWP16001 (DWP) 2 mg, metformin immediate release 1000 mg (MET) twice daily for 7 days and a single oral dose of DWP and MET at steady‐state for metformin (DWP+MET). Serial blood and interval urine were collected for PK and PD analyses. Safety and tolerability profiles were assessed throughout the study.
Results
DWP+MET displayed increased peak concentration and area under the concentration–time curve from time 0 to time of the last quantifiable concentration compared with DWP (per standard bioequivalence boundaries, 0.8–1.25); the geometric mean ratios (GMRs) and their 90% confidence intervals (CIs) were 1.22 (1.13–1.31) and 1.09 (1.05–1.14), respectively. DWP+MET and MET showed similar peak concentration and area under the concentration–time curve within a dosing interval at steady state for metformin; the GMRs and 90% CIs were 0.98 (0.90–1.06) and 1.05 (0.98–1.13), respectively. The amount of urinary glucose excretion from time 0 to 144 h was also comparable between DWP+MET and DWP (GMR and 90% CI; 0.99, 0.94–1.05).
Conclusion
The results suggest that DWP16001 and metformin could be coadministered without clinically relevant PK and PD interactions.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
OpenCL is an open standard to write parallel applications for heterogeneous computing systems. Since its usage is restricted to a single operating system instance, programmers need to use a mix of ...OpenCL and MPI to program a heterogeneous cluster. In this paper, we introduce an MPI-OpenCL implementation of the LINPACK benchmark for a cluster with multi-GPU nodes. The LINPACK benchmark is one of the most widely used benchmark applications for evaluating high performance computing systems. Our implementation is based on High Performance LINPACK (HPL) and uses the blocked LU decomposition algorithm. We address that optimizations aimed at reducing the overhead of CPUs are necessary to overcome the performance gap between the CPUs and the multiple GPUs. Our LINPACK implementation achieves 93.69 Tflops (46 percent of the theoretical peak) on the target cluster with 49 nodes, each node containing two eight-core CPUs and four GPUs.
SnuCL Kim, Jungwon; Seo, Sangmin; Lee, Jun ...
Proceedings of the 26th ACM international conference on Supercomputing,
06/2012
Conference Proceeding
In this paper, we propose SnuCL, an OpenCL framework for heterogeneous CPU/GPU clusters. We show that the original OpenCL semantics naturally fits to the heterogeneous cluster programming ...environment, and the framework achieves high performance and ease of programming. The target cluster architecture consists of a designated, single host node and many compute nodes. They are connected by an interconnection network, such as Gigabit Ethernet and InfiniBand switches. Each compute node is equipped with multicore CPUs and multiple GPUs. A set of CPU cores or each GPU becomes an OpenCL compute device. The host node executes the host program in an OpenCL application. SnuCL provides a system image running a single operating system instance for heterogeneous CPU/GPU clusters to the user. It allows the application to utilize compute devices in a compute node as if they were in the host node. No communication API, such as the MPI library, is required in the application source. SnuCL also provides collective communication extensions to OpenCL to facilitate manipulating memory objects. With SnuCL, an OpenCL application becomes portable not only between heterogeneous devices in a single node, but also between compute devices in the cluster environment. We implement SnuCL and evaluate its performance using eleven OpenCL benchmark applications.
In this paper, we propose an OpenCL framework for heterogeneous CPU/GPU clusters, and show that the framework achieves both high performance and ease of programming. The framework provides an ...illusion of a single system for the user. It allows the application to utilize multiple heterogeneous compute devices, such as multicore CPUs and GPUs, in a remote node as if they were in a local node. No communication API, such as the MPI library, is required in the application source. We implement the OpenCL framework and evaluate its performance on a heterogeneous CPU/GPU cluster that consists of one host node and nine compute nodes using eleven OpenCL benchmark applications.
This paper presents simple and efficient optimization techniques for an OpenCL compiler that targets reconfigurable processors. The target architecture consists of a generalpurpose processor core and ...an embedded reconfigurable accelerator with vector units. The accelerator is able to switch its architecture between the VLIW mode and the Coarse Grained Reconfigurable Array (CGRA) mode to achieve high performance. One big problem of this architecture is programming difficulty and OpenCL can be a good solution. However, since OpenCL does not guarantee performance portability, hardware dependent optimization is still necessary. Hence, we develop an OpenCL compiler framework that exploits the mode switching capability and vector units. To measure the effectiveness of the techniques, we have implemented the OpenCL framework and evaluate their performance with fourteen OpenCL benchmark applications.
In this paper, we propose an OpenCL framework for heterogeneous CPU/GPU clusters, and show that the framework achieves both high performance and ease of programming. The framework provides an ...illusion of a single system for the user. It allows the application to utilize multiple heterogeneous compute devices, such as multicore CPUs and GPUs, in a remote node as if they were in a local node. No communication API, such as the MPI library, is required in the application source. We implement the OpenCL framework and evaluate its performance on a heterogeneous CPU/GPU cluster that consists of one host node and nine compute nodes using eleven OpenCL benchmark applications.
