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SSD-Z Submission Details General Vendor: Unknown Model: Released: Technical Capacity: 256 GB (256,061 MB) Tech: Cells: NAND: Controller: Phison PS3105-S5-I Internals Model String: SSD0256XQC82X200M34237 Firmware: S5FAM018 Capabilities: APM, LBA48, SMART, NCQ, TRIM Bus Type: SATA 2.6 ATA Version: ATA8-ACS Device Type: SSD Other WWN: 0-000000-000000000 SSD-Z Version: 16.09.09 User Comment: Windows Version: 10.0.19042 Known Device: 1 Index Name Value Worst Thresh Raw Formatted 1 Raw Read Error Rate 100 100 50 7767 7 767 9 Power-on Hours (POH) 100 100 0 17040 710d 0h 12 Power Cycle Count 100 100 0 2933 2 933 168 SATA PHY Error Count 100 100 0 0 170 Reserve Block Count 100 100 10 8589934839 8 589 934 839 173 Average Block Erase Count 100 100 0 667173987 667 173 987 192 Unsafe Shutdown Count (?) 100 100 0 169 169 218 CRC Error Count 100 100 50 0 241 Lifetime GB Written 100 100 0 14339 14,003 TB Sign in / Register New Forum Posts Popular Reviews Mar 7th, 2025 XFX Radeon RX 9070 XT Mercury OC Magnetic Air Review Mar 11th, 2025 AMD Ryzen 9 9950X3D Review - Great for Gaming and Productivity Mar 5th, 2025 Sapphire Radeon RX 9070 XT Nitro+ Review - Beating NVIDIA Mar 5th, 2025 ASUS Radeon RX 9070 TUF OC Review Mar 7th, 2025 MSI MAG B850 Tomahawk Max Wi-Fi Review Nov 6th, 2024 AMD Ryzen 7 9800X3D Review - The Best Gaming Processor Mar 4th, 2025 NVIDIA GeForce RTX 5070 Founders Edition Review Mar 11th, 2025 Dough Spectrum Black 32 Review Mar 6th, 2025 Corsair Vengeance RGB CUDIMM DDR5-8800 48 GB CL42 Review Mar 10th, 2025 XPG Starker Air BTF Review Controversial News Posts NVIDIA GeForce RTX 50 Cards Spotted with Missing ROPs, NVIDIA Confirms the Issue, Multiple Vendors Affected (513) AMD Radeon RX 9070 and 9070 XT Listed On Amazon - One Buyer Snags a Unit (261) AMD RDNA 4 and Radeon RX 9070 Series Unveiled: $549 & $599 (260) AMD Mentions Sub-$700 Pricing for Radeon RX 9070 GPU Series, Looks Like NV Minus $50 Again (248) NVIDIA Investigates GeForce RTX 50 Series "Blackwell" Black. D: Workloads winget-pkgs [master ≡ 0 ~1 -0 !] winget download -m . manifests t TechPowerUp GPU-Z 2.55.0 已找到 TechPowerUp GPU-Z [TechPowerUp.GPU-Z] 版本 2.

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This example shows how to use GPU-enabled MATLAB® functions to compute a well-known mathematical construction: the Mandelbrot set. Check your GPU using the gpuDevice function.Define the parameters. The Mandelbrot algorithm iterates over a grid of real and imaginary parts. The following code defines the number of iterations, grid size, and grid limits.maxIterations = 500;gridSize = 1000;xlim = [-0.748766713922161, -0.748766707771757];ylim = [ 0.123640844894862, 0.123640851045266]; You can use the gpuArray function to transfer data to the GPU and create a gpuArray, or you can create an array directly on the GPU. gpuArray provides GPU versions of many functions that you can use to create data arrays, such as linspace. For more information, see Create GPU Arrays Directly. x = gpuArray.linspace(xlim(1),xlim(2),gridSize);y = gpuArray.linspace(ylim(1),ylim(2),gridSize);whos x y Name Size Bytes Class Attributes x 1x1000 8000 gpuArray y 1x1000 8000 gpuArray Many MATLAB functions support gpuArrays. When you supply a gpuArray argument to any GPU-enabled function, the function runs automatically on the GPU. For more information, see Run MATLAB Functions on a GPU. Create a complex grid for the algorithm, and create the array count for the results. To create this array directly on the GPU, use the ones function, and specify 'gpuArray'.[xGrid,yGrid] = meshgrid(x,y);z0 = complex(xGrid,yGrid);count = ones(size(z0),'gpuArray');The following code implements the Mandelbrot algorithm using GPU-enabled functions. Because the code uses gpuArrays, the calculations happen on the GPU.z = z0;for n = 0:maxIterations z = z.*z + z0; inside = abs(z) endcount = log(count);When computations are done, plot the results.imagesc(x,y,count)colormap([jet();flipud(jet());0 0 0]);axis off See AlsogpuArray

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Pip install taichi # Install Taichi Langti gallery # Launch demo galleryWhat is Taichi Lang?Taichi Lang is an open-source, imperative, parallel programming language for high-performance numerical computation. It is embedded in Python and uses just-in-time (JIT) compiler frameworks, for example LLVM, to offload the compute-intensive Python code to the native GPU or CPU instructions. The language has broad applications spanning real-time physical simulation, numerical computation, augmented reality, artificial intelligence, vision and robotics, visual effects in films and games, general-purpose computing, and much more. ...MoreWhy Taichi Lang?Built around Python: Taichi Lang shares almost the same syntax with Python, allowing you to write algorithms with minimal language barrier. It is also well integrated into the Python ecosystem, including NumPy and PyTorch.Flexibility: Taichi Lang provides a set of generic data containers known as SNode (/ˈsnoʊd/), an effective mechanism for composing hierarchical, multi-dimensional fields. This can cover many use patterns in numerical simulation (e.g. spatially sparse computing).Performance: With the @ti.kernel decorator, Taichi Lang's JIT compiler automatically compiles your Python functions into efficient GPU or CPU machine code for parallel execution.Portability: Write your code once and run it everywhere. Currently, Taichi Lang supports most mainstream GPU APIs, such as CUDA and Vulkan.... and many more features! A cross-platform, Vulkan-based 3D visualizer, differentiable programming, quantized computation (experimental), etc.Getting StartedInstallation PrerequisitesOperating systemsWindowsLinuxmacOSPython: 3.6 ~ 3.10 (64-bit only)Compute backendsx64/ARM CPUsCUDAVulkanOpenGL (4.3+)Apple MetalWebAssembly (experiemental) Use Python's package installer pip to install Taichi Lang:pip install --upgrade taichiWe also provide a nightly package. Note that nightly packages may crash because they are not fully tested. We cannot guarantee their validity, and you are at your own risk trying out our latest, untested features. The nightly packages can be installed from our self-hosted PyPI (Using self-hosted PyPI allows us to provide more frequent releases over a longer period of time)pip install -i taichi-nightlyRun your "Hello, world!"Here is how you can program a 2D fractal in Taichi:# python/taichi/examples/simulation/fractal.pyimport taichi as titi.init(arch=ti.gpu)n = 320pixels = ti.field(dtype=float, shape=(n * 2, n))@ti.funcdef complex_sqr(z): return ti.Vector([z[0]**2 - z[1]**2, z[1] * z[0] * 2])@ti.kerneldef paint(t: float): for i, j in pixels: # Parallelized over all pixels c = ti.Vector([-0.8, ti.cos(t) * 0.2]) z = ti.Vector([i / n - 1, j / n - 0.5]) * 2 iterations = 0 while z.norm() 20 and iterations 50: z = complex_sqr(z) + c iterations += 1 pixels[i, j] = 1 - iterations * 0.02gui = ti.GUI("Julia Set", res=(n * 2, n))for

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