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The TMPI measured time management using seven practice constructs.
Two points of contact were used to collect the TMPI. All beginning teachers were sent an e-mail in January of 2009 with a link to complete the instrument online.
The JSS was collected immediately following the TMPI data collection using the original frame of 36 teachers and utilized three points of contact.
While there is room for improvement and calls for intervention may be appropriate, it should be noted that compared to the normative data provided by the TMPI (Pfaff, 2000), beginning agriculture teachers in Missouri appear to be more effective at managing their time.
Missing data Table 2 Time Management Practices of Beginning Agriculture Teachers (n = 32) Teacher M/P Data Norm Data Grand Mean Mean TMPI Practice Mean (c) Total SD Range Total SD Meeting 5.92 23.69 3.06 14-28 23.00 4.00 Deadlines (b) Self-Confidence (b) 5.66 22.66 3.03 16-27 21.00 3.50 Setting 5.59 22.38 2.84 17-27 22.00 4.10 Priorities (b) Planning (a) 5.03 25.13 5.36 14-35 23.00 7.00 Taking Action (a) 4.67 23.34 3.60 15-29 22.00 5.00 Paperwork (a) 4.61 23.06 4.10 14-31 21.00 5.00 Resisting 4.48 17.94 3.23 9-23 17.00 4.00 Involvement (b) Note.
We have implemented a prototype system called TMPI on SGI machines to demonstrate the effectiveness of our techniques.
Note that both SGI MPI and MPICH have implemented all MPI 1.1 functions; however those additional functions are independent, and integrating them into TMPI should not affect our experimental results.
Figure 12 depicts the overall performance of TMPI, SCI, and MPICH in a dedicated environment at UCSB.
From the result shown in Figure 12, we can see that TMPI is competitive with SGI MPI.
Memory Other cost (seconds) Kernel copy sync included Synchronization TMPI 11.14 0.82 1.50 0.09 SGI MPI 11.29 1.79 7.30 -- MPICH 11.21 1.24 7.01 4.96 To further examine the scalability and competitiveness of TMPI, we have conducted additional experiments in an Origin 2000 machine at NCSA using up to 64 processors.
Our evaluation methodology is to create a repeatable nondedicated setting on dedicated processors so that the MPICH and SGI versions can be compared with TMPI. What we did was to manually assign a fixed number of MPI nodes to each idle physical processor,(4) then vary this number to check performance sensitivity.
Figure 14 shows the speedup of TMPI code for three benchmarks when the number of MPI nodes per processor increases.
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