Exemple #1
0
func main() {
	nimble.Init()
	defer nimble.Cleanup()
	nimble.SetOD("demag1.out")
	gpu.LockCudaThread()

	N0, N1, N2 := 1, 3*64, 5*64
	cx, cy, cz := 3e-9, 3.125e-9, 3.125e-9
	mesh := nimble.NewMesh(N0, N1, N2, cx, cy, cz)
	fmt.Println("mesh:", mesh)

	mbox := gpu.NewConst("m", "", mesh, nimble.UnifiedMemory, []float64{1, 0, 0})
	m := mbox.Output()

	const acc = 4
	kernel := mag.BruteKernel(mesh, acc)
	conv := gpu.NewConvolution("B", "T", mesh, nimble.UnifiedMemory, kernel, m)
	B := conv.Output()

	const probe = 24 * 121
	outputc := B.NewReader()
	nimble.RunStack()
	output := host(outputc.ReadNext(mesh.NCell()))
	if output[0][probe] > -0.97 || output[0][probe] < -0.99 || output[1][probe] != 0 || output[2][probe] != 0 {
		fmt.Println("failed, got:", output[0][probe])
		os.Exit(2)
	} else {
		fmt.Println("OK")
	}
}
Exemple #2
0
// Precision test for the kernel: thin film.
func main() {
	nimble.Init()
	defer nimble.Cleanup()
	nimble.SetOD("kernel-film.out")
	gpu.LockCudaThread()

	Y, X := core.IntArg(0), core.IntArg(1)
	y, x := float64(Y), float64(X)
	N0, N1, N2 := 1, 1024/Y, 1024/X
	cx, cy, cz := 1e-9, 1e-9*y, 1e-9*x
	mesh := nimble.NewMesh(N0, N1, N2, cx, cy, cz)
	fmt.Println("mesh:", mesh)

	mbox := gpu.NewConst("m", "", mesh, nimble.UnifiedMemory, []float64{1, 0, 0})
	m := mbox.Output()

	acc := 4.
	kernel := mag.BruteKernel(mesh, acc)
	conv := gpu.NewConvolution("B", "T", mesh, nimble.UnifiedMemory, kernel, m)
	B := conv.Output()

	outputc := B.NewReader()
	nimble.RunStack()
	output := host(outputc.ReadNext(mesh.NCell()))
	Bz := core.Reshape(output[0], [3]int{N0, N1, N2})
	probe := float64(Bz[N0/2][N1/2][N2/2])
	fmt.Println(probe)
	want := -1.
	if math.Abs(probe-want) > 0.01 {
		fmt.Println("FAIL")
		os.Exit(2)
	} else {
		fmt.Println("OK")
	}
}
Exemple #3
0
func main() {
	nimble.Init()
	defer nimble.Cleanup()
	nimble.SetOD("demag2.out")

	gpu.LockCudaThread()

	N0, N1, N2 := 4, 32, 1024
	cx, cy, cz := 3e-9, 3.125e-9, 3.125e-9
	mesh := nimble.NewMesh(N0, N1, N2, cx, cy, cz)
	fmt.Println("mesh:", mesh)

	mbox := gpu.NewConst("m", "", mesh, nimble.UnifiedMemory, []float64{1, 0, 0})
	m := mbox.Output()

	const acc = 4
	kernel := mag.BruteKernel(mesh, acc)
	conv := gpu.NewConvolution("B", "T", mesh, nimble.UnifiedMemory, kernel, m)
	B := conv.Output()

	outputc := B.NewReader()
	nimble.RunStack()
	output := host(outputc.ReadNext(mesh.NCell()))

	out0 := core.Reshape(output[0], mesh.Size())
	out1 := core.Reshape(output[1], mesh.Size())
	out2 := core.Reshape(output[2], mesh.Size())
	X, Y, Z := N0/2, N1/2, N2/2
	if out0[X][Y][Z] < -0.95 || out0[X][Y][Z] > -0.90 || out1[X][Y][Z] > 0.001 || out2[X][Y][Z] > 0.001 {
		fmt.Println("failed, got:", out0[X][Y][Z], out1[X][Y][Z], out2[X][Y][Z])
		os.Exit(2)
	} else {
		fmt.Println("OK")
	}
}
Exemple #4
0
// Precision test for the kernel: cube.
func main() {
	nimble.Init()
	core.LOG = false
	defer nimble.Cleanup()
	nimble.SetOD("kernel-cube.out")

	N0, N1, N2 := 2, 2, 2
	cx, cy, cz := 1e-9, 1e-9, 1e-9
	mesh := nimble.NewMesh(N0, N1, N2, cx, cy, cz)
	fmt.Println("mesh:", mesh)

	for acc := 1; acc < 10; acc++ {
		mag.BruteKernel(mesh, float64(acc))
		//fmt.Println(acc, kernel[0][0][0][0][0], kernel[1][0][0][0][0])
	}
}
Exemple #5
0
func main() {
	nimble.Init()
	defer nimble.Cleanup()
	nimble.SetOD("gpu4.out")

	mem := nimble.GPUMemory

	const (
		a          = 8
		N0, N1, N2 = 1, 32 * a, 128 * a
		c          = 1e-9
		cx, cy, cz = c, c, c
		Bsat       = 800e3 * mag.Mu0
		Aex_red    = 13e-12 / (Bsat / mag.Mu0)
		α          = 1
	)

	mesh := nimble.NewMesh(N0, N1, N2, cx, cy, cz)
	fmt.Println("mesh:", mesh)

	m := nimble.MakeChanN(3, "m", "", mesh, mem, 0)
	M := gpu.Device3(m.ChanN().UnsafeData())
	M[0].Memset(float32(1 / math.Sqrt(3)))
	M[1].Memset(float32(1 / math.Sqrt(3)))
	M[2].Memset(float32(1 / math.Sqrt(3)))

	const acc = 1
	kernel := mag.BruteKernel(mesh, acc)
	B := gpu.NewConvolution("B", "T", mesh, mem, kernel, m).Output()
	Bex := gpu.NewExchange6("Bex", m, Aex_red).Output()

	BeffBox := gpu.NewSum("Beff", B, Bex, Bsat, 1, mem)
	Beff := BeffBox.Output()

	tBox := gpu.NewLLGTorque("torque", m, Beff, α)
	torque := tBox.Output()

	solver := gpu.NewHeun(m, torque, 10e-15, mag.Gamma0)
	solver.Maxerr = 2e-4
	solver.Mindt = 1e-15

	solver.Steps(100)
}
Exemple #6
0
// Standard problem 4 on GPU
func main() {
	nimble.Init()
	defer nimble.Cleanup()
	nimble.SetOD("gpu4-3d.out")
	gpu.LockCudaThread()

	mem := nimble.GPUMemory

	const (
		N0, N1, N2 = 1 * 2, 32, 128
		Sx, Sy, Sz = 3e-9, 125e-9, 500e-9
		cx, cy, cz = Sx / N0, Sy / N1, Sz / N2
		Bsat       = 800e3 * mag.Mu0
		Aex_red    = 13e-12 / (Bsat / mag.Mu0)
		α          = 1
	)

	mesh := nimble.NewMesh(N0, N1, N2, cx, cy, cz)
	fmt.Println("mesh:", mesh)

	// TODO: MakeChanN -> NewQuant()
	m := nimble.MakeChanN(3, "m", "", mesh, mem, 0)
	M := gpu.Device3(m.ChanN().UnsafeData())
	M[0].Memset(float32(1 / math.Sqrt(3)))
	M[1].Memset(float32(1 / math.Sqrt(3)))
	M[2].Memset(float32(1 / math.Sqrt(3)))

	acc := 5.
	kernel := mag.BruteKernel(mesh, acc)
	B := gpu.NewConvolution("B", "T", mesh, mem, kernel, m).Output()

	exch := gpu.NewExchange6("Bex", m, Aex_red)
	Bex := exch.Output()

	BeffBox := gpu.NewSum("Beff", B, Bex, Bsat, 1, mem)
	Beff := BeffBox.Output()

	tBox := gpu.NewLLGTorque("torque", m, Beff, α)
	torque := tBox.Output()

	solver := gpu.NewHeun(m, torque, 10e-15, mag.Gamma0)
	solver.Maxerr = 5e-4
	solver.Maxdt = 1e-12
	solver.Mindt = 1e-15
	solver.Headroom = 0.5

	every := 100
	uni.Autosave(m, every, gpu.GPUDevice)
	uni.Autotable(m, every/10, gpu.GPUDevice)

	solver.Advance(2e-9)

	var avg [3]float32
	for i := range avg {
		avg[i] = gpu.Sum(m.UnsafeData()[i].Device()) / float32(mesh.NCell())
	}
	want := [3]float32{0, 0.12521397, 0.9669811}
	err := math.Sqrt(float64(sqr(avg[0]-want[0]) + sqr(avg[1]-want[1]) + sqr(avg[2]-want[2])))
	fmt.Println("avg:", avg, "err:", err)
	if err > 1e-2 {
		fmt.Println("FAILED")
		os.Exit(2)
	}
	fmt.Println("OK")

	const (
		Bx = -24.6E-3
		By = 4.3E-3
		Bz = 0
	)
	Bext := gpu.NewConst("Bext", "T", mesh, mem, []float64{Bz, By, Bx}).Output()
	BeffBox.MAdd(Bext, 1)
	tBox.Alpha = 0.02
	solver.Advance(1e-9)

	for i := range avg {
		avg[i] = gpu.Sum(m.UnsafeData()[i].Device()) / float32(mesh.NCell())
	}

	want = [3]float32{0.04303933, 0.13000599, -0.9842051}
	err = math.Sqrt(float64(sqr(avg[0]-want[0]) + sqr(avg[1]-want[1]) + sqr(avg[2]-want[2])))
	fmt.Println("avg:", avg, "err:", err)
	if err > 1e-2 {
		fmt.Println("FAILED")
		os.Exit(2)
	}
	fmt.Println("OK")
}
Exemple #7
0
func main() {
	nimble.Init()
	defer nimble.Cleanup()
	nimble.SetOD("gpueuler.out")

	N0, N1, N2 := 1, 32, 128
	cx, cy, cz := 3e-9, 3.125e-9, 3.125e-9
	mesh := nimble.NewMesh(N0, N1, N2, cx, cy, cz)
	fmt.Println("mesh:", mesh)

	m := nimble.MakeChanN(3, "m", "", mesh, nimble.UnifiedMemory, 0)
	fmt.Println("m:", m)

	acc := 8
	kernel := mag.BruteKernel(mesh, acc)
	B := conv.NewSymm2D("B", "T", mesh, nimble.UnifiedMemory, kernel, m).Output()

	const (
		Bsat = 1.0053
		aex  = mag.Mu0 * 13e-12 / Bsat
		α    = 1
	)
	//exch := cpu.NewExchange6("Bex", "T", nimble.UnifiedMemory, m.NewReader(), aex)
	exch := gpu.NewExchange6("Bex", m, aex)
	nimble.Stack(exch)
	Bex := exch.Output()

	//	heff := MakeChan3("Heff", "", mesh)
	Beff := gpu.NewSum("Beff", B, Bex, Bsat, 1, nimble.UnifiedMemory).Output()

	tBox := gpu.NewLLGTorque("torque", m, Beff, α)
	nimble.Stack(tBox)
	torque := tBox.Output()

	const dt = 100e-15

	solver := gpu.NewEuler(m, torque, mag.Gamma0, dt)

	M := cpu.Host(m.ChanN().UnsafeData())
	for i := range M[2] {
		M[2][i] = 1
		M[1][i] = 0.1
	}

	every := 100
	nimble.Autosave(B, every)
	nimble.Autosave(m, every)
	nimble.Autosave(Bex, every)
	nimble.Autosave(Beff, every)
	nimble.Autosave(torque, every)
	nimble.Autotable(m, every)

	nimble.RunStack()

	solver.Steps(100)
	res := cpu.Host(m.ChanN().UnsafeData())
	got := [3]float32{res[0][0], res[1][0], res[2][0]}
	expect := [3]float32{-0.033120323, 0.20761484, 0.9776498}
	solver.Steps(10000)
	fmt.Println("result:", got)
	if got != expect {
		fmt.Println("expected:", expect)
		os.Exit(2)
	} else {
		fmt.Println("OK")
	}
}
Exemple #8
0
func main() {
	nimble.Init()
	defer nimble.Cleanup()
	nimble.SetOD("test4.out")

	N0, N1, N2 := 1, 32, 128
	cx, cy, cz := 3e-9, 3.125e-9, 3.125e-9
	mesh := nimble.NewMesh(N0, N1, N2, cx, cy, cz)
	fmt.Println("mesh:", mesh)

	m := nimble.MakeChanN(3, "m", "", mesh, nimble.UnifiedMemory, 0)
	fmt.Println("m:", m)

	acc := 8
	kernel := mag.BruteKernel(mesh, acc)
	B := gpu.NewConvolution("B", "T", mesh, nimble.UnifiedMemory, kernel, m).Output()

	const Bsat = 1.0053
	const aex = mag.Mu0 * 13e-12 / Bsat
	exch := cpu.NewExchange6("Bex", "T", nimble.UnifiedMemory, m.NewReader(), aex)
	Bex := exch.Output()

	Beff := cpu.NewSum("Beff", B, Bex, Bsat, 1, nimble.UnifiedMemory).Output()

	const alpha = 1
	tbox := cpu.NewLLGTorque("torque", m, Beff, alpha)
	nimble.Stack(tbox)
	torque := tbox.Output()

	const dt = 100e-15

	solver := cpu.NewEuler(m, torque.NewReader(), mag.Gamma0, dt)

	M := cpu.Host(m.ChanN().UnsafeData())
	for i := range M[2] {
		M[2][i] = 1
		M[1][i] = 0.1
	}

	every := 100
	uni.Autosave(B, every, cpu.CPUDevice)
	uni.Autosave(m, every, cpu.CPUDevice)
	uni.Autosave(Bex, every, cpu.CPUDevice)
	uni.Autosave(Beff, every, cpu.CPUDevice)
	uni.Autosave(torque, every, cpu.CPUDevice)
	uni.Autotable(m, every, cpu.CPUDevice)

	nimble.RunStack()

	solver.Steps(100)
	res := cpu.Host(m.ChanN().UnsafeData())
	got := [3]float32{res[0][0], res[1][0], res[2][0]}
	expect := [3]float32{-0.03450077, 0.21015842, 0.9770585}
	fmt.Println("result:", got)
	if got != expect {
		fmt.Println("expected:", expect)
		os.Exit(2)
	} else {
		fmt.Println("OK")
	}
}
Exemple #9
0
// Standard problem 4 on GPU
func main() {
	nimble.Init()
	defer nimble.Cleanup()
	nimble.SetOD("gpu4.out")

	mem := nimble.GPUMemory

	const (
		N0, N1, N2 = 1, 32 * 2, 128
		cx, cy, cz = 3e-9, 3.125e-9 / 2, 3.125e-9
		Bsat       = 1.0053
		Aex_red    = mag.Mu0 * 13e-12 / Bsat
		α          = 1
	)

	mesh := nimble.NewMesh(N0, N1, N2, cx, cy, cz)
	fmt.Println("mesh:", mesh)

	// TODO: MakeChanN -> NewQuant()
	m := nimble.MakeChanN(3, "m", "", mesh, mem, 0)
	M := gpu.Device3(m.ChanN().UnsafeData())
	M[0].Memset(0)
	M[1].Memset(0.1)
	M[2].Memset(0.99)

	acc := 10
	kernel := mag.BruteKernel(mesh, acc)
	B := gpu.NewConvolution("B", "T", mesh, mem, kernel, m).Output()

	exch := gpu.NewExchange6("Bex", m, Aex_red)
	nimble.Stack(exch)
	Bex := exch.Output()

	BeffBox := gpu.NewSum("Beff", B, Bex, Bsat, 1, mem)
	Beff := BeffBox.Output()

	tBox := gpu.NewLLGTorque("torque", m, Beff, α)
	nimble.Stack(tBox)
	torque := tBox.Output()

	solver := gpu.NewHeun(m, torque, 1e-15, mag.Gamma0)

	every := 100
	uni.Autosave(m, every, gpu.GPUDevice)
	uni.Autotable(m, every/10, gpu.GPUDevice)

	solver.Advance(0.3e-9)

	//	res := cpu.Host(m.ChanN().UnsafeData())
	//	got := [3]float32{res[0][0], res[1][0], res[2][0]}
	//	expect := [3]float32{1.090642e-06, 0.6730072, 0.739636}
	//	fmt.Println("result:", got)
	//	if got != expect {
	//		fmt.Println("expected:", expect)
	//		os.Exit(2)
	//	} else {
	//		fmt.Println("OK")
	//	}

	const (
		Bx = -24.6E-3
		By = 4.3E-3
		Bz = 0
	)
	Bext := gpu.RunConst("Bext", "T", mesh, mem, []float64{Bz, By, Bx})
	BeffBox.MAdd(Bext, 1)
	tBox.SetAlpha(0.02)
	solver.Advance(1e-9)
}