User:Arash

Arash Sadrieh is working on developing GPU-based solvers for ASCEND. He is a PhD student at Murdoch University in Western Australia.

Goals

• Make ASCEND to export models(residuals and jacobian) evaluators to bintokens.
  • reinstate bintoken functionality
  • add gradient calculation support to bintoken stuff
• Prepare a large model (preferably 100,000+) and a unit test for verifying and benchmarking the NLA solver using this model.
• Develop a CUDA code generator that creates GPU-based bintokens.
• Create a new library in ascend (GPU_manager) which is responsible to manage all the GPU related tasks. Including data transfer between host and GPU, launching bintoken CUDA kernels and parallel calculation of the residuals normal (required in line-search algorithm).
• Fork a new NLA solver from current solver: In the new solver when the solver needs to evaluate a block residual or Jacobian, the call is redirected to GPU_manager.
• Wrapping appropriate functionality in ascend solver interface that decouples GPU manager from the solver. (The interface should provide batch residual (and Jacobian) evaluation for group of relations).
• Benchmark the results and probably switch to other many (or multi) core architectures and languages.

Progress

fill in here

Test models

The following test model is created to test the GPU-based bintokens, the models are proposed by Ben Allan.

Large Distillation Column Model

MODEL c4_10_demo_column() REFINES test_demo_column(); 
        demo IS_A 
        demo_column(['n_butane','n_pentane','n_hexane','n_heptane','n_octane','n_nonane','n_decane'],'n_decane',100,51);
METHODS 
END c4_10_demo_column;