convexbrain
convexbrain
A benchmark result of LP * https://github.com/convexbrain/Totsu/tree/1f5200599ffd8bdf15e6ce672bcc1c2f0bbc11bb/experimental/benchmark_lp * **F32CUDA is faster than FloatGeneric.**  * CPU * Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz * RAM: 32.0 GB *...
A benchmark result of QP * https://github.com/convexbrain/Totsu/tree/884e36b4fd32d696ddca046af755ad8a2d120a61/experimental/benchmark_qp * **F32CUDA is slower than FloatGeneric.** 😠 Proceed to profiling using this benchmark.
A profiling result of QP benchmark * Many memory accesses are occurring when projecting onto the cone. 
https://github.com/convexbrain/Totsu/tree/b56407463b691a3f2418510bc43e8a72d5186fc1/experimental/benchmark_qp * CUDA-izing projection onto cones as much as possible. * 200 vars (100 primals, 100 duals). 
* 400 vars (200 primals, 200 duals). 
https://github.com/convexbrain/Totsu/tree/77f0e5cc10e7a2d29567352f88135a99ed620be1/experimental/benchmark_qp * FxHashMap instead of HashMap. * 200 vars (100 primals, 100 duals). 
https://github.com/convexbrain/Totsu/tree/13b8d378f79445c53b9c9f77fbf4389029423d12/experimental/benchmark_qp * Intermittent criteria checks. * 200 vars (100 primals, 100 duals). 
 * The effect of CUDA comes out from about 800 variables. * The number of iterations is not monotonically increasing; probably because those QPs are generated...