You are viewing an old version of this page. View the current version.
Compare with Current
View Page History
« Previous
Version 29
Next »
To-Do List
3/14/2012
- private svn (cpo)
- try nvvp/transpose (or C60 with more grid points) for >5 minutes (lin)
- send mail to nvidia or list to understand why nvvp profile cuts off after 5 minutes (lin)
- understand bottleneck in get_wfs (jun)
- implement fft/gemv (cpo)
- is there a cuda library for trace like zgeev (cpo)
- run a 3x3x3 system to see if bottlenecks stay the same (cpo)
- driver hang status (cpo)
- understand how to fix gs.py bottlenecks in more detail (lin/cpo) using gpaw profiler:
- pseudo density: density.py: self.calculate_pseudo_density(wfs) (cpo)
- projections: overlap.py: wfs.pt.integrate(psit_nG, P_ani, kpt.q) (cpo)
- RMM-DIIS: eigensolvers/rmm_diis.py: lots of lines (cpo)
- projections: eigensolvers/rmm_diis.py: wfs.pt.integrate(dpsit_xG, P_axi, kpt.q) (lin)
- calc_h_matrix: eigensolvers/eigensolver.py: H_nn = self.operator.calculate_matrix_elements, hamiltonian.xc.correct_hamiltonian_matrix (lin)
- rotate_psi: eigensolvers/eigensolver.py (lin)
General Topics
- Stanford CUDA course: http://code.google.com/p/stanford-cs193g-sp2010/
- (Everyone) Understand gpaw (read paper)
- what other steps could we parallelize?
- Can we do existing parallelization better? (e.g. use ideas in Todd's GPU papers)
- (Everyone) Go through CUDA tutorial here.
- Understand blocks/threads/warps and how they map onto GPU hardware (details of which can be seen with "deviceQuery" command)
- (Lin) Find tool to measure:
- memory bandwidth usage
- gpu flops usage
- (Jun) :
- Parallelize LCAO/planewave/RPA (zher performance?)? non-rmm-diis eigensolver?
- merge with trunk?
- (cpo) :
- Understand code flow
- Understand where the ~23 cuda kernels are used
- Understand which bottlenecks we need to tackle
- Do another gpu-gpaw install (to learn)
- Understand Pt 3x4x3 CPU/GPU difference versus 3x2x3 (performance scaling with system size)
- Can multiple CPU processes win by using the same GPU?
- Understand pycuda
- Understand gpaw interface to cuda (c/cuda subdirectory)
- Read CUDA programming manual here.
- Do all gpaw self-tests pass with GPUs?
- Can we get bigger bang-per-buck with GeForce instead of Tesla? (don’t need GPUDirect, maybe live with less memory/bandwidth? double precision worse)
- Understand cuda better:
- Does Samuli use pinned memory correctly?
- run/understand cuda a bandwidth benchmark
- Could we use GPUdirect for MPI data transfer?
- Does the GPU performance scale with the product of gridpoints*bands? Might be a combinatorial effect with the bands, linear with the grid points?
- Duplicate Samuli results
- Update to most recent version in svn
- Understand where gpaw scaling maxes out for Pt 3x4x3
- Why is CO on 2 GPUs slower than on 8 CPUs?
- Can we do something less precise in the vacuum area? (fewer grid points?)
- Do we need a fatter interconnect for GPUs?