To-Do List
5/2/2012
- looking at EXX bottleneck (rewriting) (jun)
- use cuda streams for small RPA systems (jun)
- libxc integration (cpo)
- understand MKL benchmark (jun/cpo)
- pycuda (cpo)
- understand RPBE kernel: (lin)
- understand "double" problem
- vary np, block_size, nstreams
- loop testfunc many times
- longer term: look at jussi/samuli kernel for ideas
4/25/2012
- looking at EXX bottleneck (rewriting) (jun)
- postpone work on ZHER stuff until we have news from INCITE (jun)
- talk to Frank about computing time applications (cpo)
- understand MKL benchmark (jun/cpo)
- libxc integration (cpo)
4/18/2012
- look at reduced-scope libxc example plus RPBE (lin)
- if there is time, benchmark the RPBE kernel (lin)
- zher performance improvement with multiple streams (jun)
- make INCITE version work (jun/cpo)
- move to libxc 1.2 (cpo)
4/11/2012
- libxc parallelization (lin)
- libxc integration (cpo)
- understand missing time in cublas mode (jun/cpo)
- how to put the gemm in PW mode in a fairly neat way (lin/cpo)
- start working on multiple-alpha kernel (MAZHER) (jun/cpo)
- work on INCITE proposal (jun/cpo)
3/28/2012
- gemm (lin)
- run pt3x3 (cpo)
- libxc (cpo, and lin if he finishes gemm)
- cher/fft (jun)
- fix gpu allocation (cpo)
- circular dependency problem with monkhorst_pack (cpo)
- mpi failure with cuzher (cpo)
3/21/2012
- batch queue for GPU machine (cpo)
- fft/gemm/gemv (lin/jun/cpo)
- single precision cher instead of zher? (jun/cpo)
- new libxc (cpo)
- fix libfftw detection (cpo)
- improve zher in cuda (long project, jun/cpo)
- move "expand" from python into C, post to mailing list? (lin)
- look at spin paired (cpo)
- run pt3x3 (cpo)
3/14/2012
- pycuda compatibility (cpo)
- 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)
Accessing suncatgpu01 SVN
We have put a version of GPAW in a local SVN repository on suncatgpu01. To access it, use the following:
svn co svn://localhost svngpaw
You can put whatever you want for the last argument (local directory name).
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?