FantasyVR

import taichi as ti
import numpy as np

ti.init(arch=ti.gpu)

N = 32
dt = 1e-4
dx = 1 / N
rho = 4e1
NF = 2 * N**2  # number of faces

FantasyVR

#include <cuda_runtime_api.h> // cudaMalloc, cudaMemcpy, etc.
#include <cusparse.h>         // cusparseSpMV
#include <stdio.h>            // printf
#include <stdlib.h>           // EXIT_FAILURE

/* 
 *  cuSparse Version: 11.4.0
 * 
 *  How to compile: 
 *          nvcc -arch=sm_75  -lcusparse  cusparse_spmv.cu -o cusparse_spmv

FantasyVR

"""
从jiafeng的review： https://github.com/taichi-dev/taichi/pull/4841#discussion_r880193695 
这里我发现 内存的layout确实会影响稀疏矩阵的构建。


目前的方案是：
1. 直接获取ndarry的 data_ptr 
2. 从该指针处的数据按照[row, col, value]的三元组的内存排布创建Sparse matrix。

目前的脚本尝试着各种ndarray的内存分布。

FantasyVR

# MPM-MLS in 88 lines of Taichi code, originally created by @yuanming-hu
# P2G is parallel in this script.
import taichi as ti

ti.init(arch=ti.gpu)

n_particles = 8192
n_grid = 128
dx = 1 / n_grid
dt = 2e-4

FantasyVR

#define GLFW_INCLUDE_VULKAN
#include <GLFW/glfw3.h>

#include <iostream>
#include <stdexcept>
#include <cstdlib>
#include <vector>

#include <string>

FantasyVR

import taichi as ti

ti.init(arch=ti.cuda)
N = 5
NV = (N + 1)**2
NT = 2 * N**2
NE = 2 * N * (N + 1) + N**2
pos = ti.Vector.field(3, ti.f32, shape=NV)
tri = ti.field(ti.i32, shape=3 * NT)
edge = ti.Vector.field(2, ti.i32, shape=NE)

FantasyVR

# https://www.cs.cmu.edu/~baraff/papers/sig98.pdf
import argparse

import numpy as np

import taichi as ti


@ti.data_oriented
class Cloth:

FantasyVR

"""
Loop:
    Press start button to start random selection
    Press end to show the selected ID
"""
import taichi as ti
ti.init(arch=ti.cpu,cpu_max_num_threads=1)
n = 100
userId = ti.field(ti.i32, shape=n) # userId is an array of 0s

FantasyVR

import taichi as ti

ti.init(arch=ti.cpu, packed=True)

a = ti.field(dtype=ti.i32)
ti.root.dense(ti.ij, (2, 3)).dense(ti.ij, (2,3)).place(a)

@ti.kernel
def fill():
    for i, j in a:

FantasyVR

# https://www.cs.cmu.edu/~baraff/papers/sig98.pdf
import taichi as ti
import numpy as np
import argparse


@ti.data_oriented
class Cloth:
    def __init__(self, N):
        self.N = N