Matlab, CUDA, and GPU Computing
Main Project here: Estimating Unobserved Complementarities between Entrepreneurs and Venture Capitalists Matlab Code
Getting Started
We are running remotely on the Database Server via VNC. The VNC service on DB Server was configured by Wei during Summer 2018.
- To start/configure the VNC service on DB Server and to get connected remotely, see the documentation here.
- Once you are connected to DB Server through VNC, open a terminal on DB Server and type
matlab
This will bring up the Matlab GUI.
- To check if Matlab is working with our Nvidia graphics card, in the Matlab command window, type
gpuDevice.
What Works, and What Doesn't
- GPU computing does not work well for linear programming. The biggest reason is that GPUs don't work well for sparse linear algebra used for most LP solver. Another case study published 3 years ago shows that "the overhead of communication between CPU and GPU grows faster than the benefit of parallelizing matrix operations via CUDA." In another technical report, it is confirmed that "all algorithms (for solving LP and MIP problems)... do not fit to the SIMT paradigm".
- For the above reason, Gurobi does not support GPU computing.
- Matlab does not have GPU-based linprog or ga.
- Gurobi does support CPU parallelism.
- In general, individual threads of GPU only perform well with simple arithmetic tasks. Task-parallelizing toolboxes/libraries/packages onto threads of GPU is NO GO.
Deliverable
1.In Matlab, use parfor for both CPU and GPU to solve a set of LPs. Profile and compare. Just try to do CPU instead. Figure out how to do CPU-based parallel computing with Gurobi.
Reference
1. MATLAB GPU Computing Support for NVIDIA CUDA-Enabled GPUs
2. Getting Started with Parallel Computing Toolbox
3. Speeding Up Algorithms: When Parallel Computing and GPUs Do and Don't Accelerate
