Asian Executive Salary Trends
The surge in executive salaries in Asian countries compared to Europe and North America, driven by strong economic growth, talent scarcity, and innovation demands. The shift towards simpler pay packages and short-term incentives is predicted to reshape compensation trends in the region.
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.
E N D
Presentation Transcript
SDP benchmark suite Stefano Corda and Manuel Stutz stefano.corda@epfl.ch manuel.stutz@fhnw.ch 06-09-2023 SKACH days
SDP benchmark suite Spack software manager: https://github.com/spack/spack Reframe regression testing framework: https://github.com/reframe-hpc/reframe Conda: python environments Goals: Easy to use/deploy benchmark suite for procurement o decision for the SDP. Support DP-ART developer benchmarking. o 2
SDP-benchmark suite Basic concepts: 1. Test: What to test 2. System: Where is the test allowed to run 3. Environment: What environment is the test expecting @rfm.simple_test class FunclibDftTest(RunOnlyBenchmarkBase): def __init__(self): super().__init__() self.valid_prog_environs = ['funclib-test'] self.valid_systems = filter_systems_by_env(self.valid_prog_environs) 3
Execution pipeline @run_after / @run_before hooks @run_after('setup') def set_conda_cmds(self): self.prerun_cmds += self.env_manager.emit_conda_activate_cmds().split(';') 4
Post-run checks Sanity Phase @run_before('sanity') def set_sanity_patterns(self): patterns = [ sn.assert_not_found('error', self.stderr), sn.assert_not_found('Error', self.stderr), ] Performance phase @run_before('performance') def set_perf_patterns(self): self.perf_variables = {'elapsed_time_v00_cpu': self.extract_v00_cpu_time()} @performance_function('s') def extract_v00_cpu_time(self): return sn.extractsingle(rf'^Elapsed Time\s+(?P<elapsed_time>\S+)\s+s', self.stdout, 'elapsed_time', float, 0) 6
Previous PI improvements Progress in the last few PIs 1. Multiple statistical runs of a test 2. UsesInternet safeguard 3. Power measurement (libpowersensor) deprecated 4. CI Pipeline on Cluster 5. Extending the benchmark suite o Hippo Funclib Test o IDG Micro Test o FFT CPU / GPU Test 7
PI19 update Jean Zay (French Server): System configuration LOW self-cal (calibrate 1) runs (with hacks) Grid5000 (French Server): System configuration Basic tests CSCS Piz Daint: Integration (with hacks) of profiling libraries (PMT/RUM) & performance visualization utilities Hacks = not clean solution (NOT mergeable) 8
Improvement items PI20 onward (WIP & Q/A) Simplify Benchmark Suite structure (easy to deploy on new systems and to hand over to HW vendors). Integrate profiling libraries and tools (VTune, Nsight, etc.) wrapping run phase (running on compute nodes). Remove system configurations (a script should be able to create and log the system configuration). Remove/Improve conda manager (conflicts with python packages installed with spack). 9
PI20 plan Assess the reproducibility of benchmark results and bias induced by benchmarking overhead. Identify how to normalize outputs of benchmarking and corresponding data analysis scripts. Complete assessment of the state of applications in the benchmark suite. Produce a roadmap for further developing the benchmark suite. 10
