Gpu z 2 51 0

Author: a | 2025-04-24

CPU-Z, GPU-Z, Core Temp, SpeedFan (Area-51 R2) CPU-Z and GPU-Z are great for showing you data about your Alienware hardware. Below are the shots from Area-51 R2

CPU-Z, GPU-Z, Core Temp, SpeedFan (Area-51 R2)

400000:05:30: ------------------------------------------00:05:30: MMDeviceAudioSource: Frequency for device 'Speakers (2- ASUS Xonar DX Audio Device)' is 352800, samples per sec is 4410000:05:30: MMDeviceAudioSource: Frequency for device 'Microphone (2- ASUS Xonar DX Audio Device)' is 352800, samples per sec is 4410000:05:32: Using RTMP service: Twitch / Justin.tv00:05:32: Server selection: rtmp://live-jfk.justin.tv/app00:05:32: Interface: Intel(R) 82579V Gigabit Network Connection (ethernet, 1000 mbps)00:05:32: Completed handshake with rtmp://live-jfk.justin.tv/app in 211 ms.00:05:33: SO_SNDBUF was at 26136000:05:33: SO_SNDBUF is now 6553600:05:42: SharedTexCapture hooked00:05:51: GraphicsCaptureSource::NewCapture: eliminating old capture00:05:51: SharedTexCapture hooked00:06:15: GraphicsCaptureSource::NewCapture: eliminating old capture00:06:15: SharedTexCapture hooked00:06:17: RTMPPublisher::SocketLoop: Increasing send buffer to ISB 262144 (buffer: 0 / 532480)00:06:51: Total frames encoded: 4836, total frames duplicated: 141 (2.92%)00:06:51: Total frames rendered: 4734, number of late frames: 12 (0.25%) (it's okay for some frames to be late)00:06:51: RTMPPublisher::SocketLoop: Graceful loop exit00:06:51: Average send payload: 7030 bytes, average send interval: 13 ms00:06:51: Number of times waited to send: 0, Waited for a total of 0 bytes00:06:51: Number of b-frames dropped: 0 (0%), Number of p-frames dropped: 0 (0%), Total 0 (0%)00:06:51: Number of bytes sent: 4036858500:06:51: 00:06:51: Profiler time results:00:06:51: 00:06:51: ==============================================================00:06:51: video thread frame - [100%] [avg time: 0.854 ms] [children: 11.5%] [unaccounted: 88.5%]00:06:51: | scene->Preprocess - [0.117%] [avg time: 0.001 ms]00:06:51: | GPU download and conversion - [11.4%] [avg time: 0.097 ms] [children: 10.1%] [unaccounted: 1.29%]00:06:51: | | flush - [8.31%] [avg time: 0.071 ms]00:06:51: | | CopyResource - [1.52%] [avg time: 0.013 ms]00:06:51: | | conversion to 4:2:0 - [0.234%] [avg time: 0.002 ms]00:06:51: Convert444Threads - [100%] [avg time: 0.418 ms] [children: 99.5%] [unaccounted: 0.478%]00:06:51: | Convert444toNV12 - [99.5%] [avg time: 0.416 ms]00:06:51: encoder thread frame - [100%] [avg time: 0.831 ms]00:06:51: ==============================================================00:06:51: 00:06:51: 00:06:51: Profiler CPU results:00:06:51: 00:06:51: ==============================================================00:06:51: video thread frame - [cpu time: avg 0.052 ms, total 249.601 ms] [avg calls per frame: 1]00:06:51: | scene->Preprocess - [cpu time: avg 0 ms, total 0 ms] [avg calls per frame: 1]00:06:51: | GPU download and conversion - [cpu time: avg 0.006 ms, total 31.2 ms] [avg calls per frame: 1]00:06:51: | | flush - [cpu time: avg 0.003 ms, total 15.6 ms] [avg calls per frame: 1]00:06:51: | | CopyResource - [cpu time: avg 0 ms, total 0 ms] [avg calls per frame: 1]00:06:51: | | conversion to 4:2:0 - [cpu time: avg 0 ms, total 0 ms] [avg calls per frame: 1]00:06:51: Convert444Threads - [cpu time: avg 0.121 ms, total 1138.81 ms] [avg calls per frame: 2]00:06:51: | Convert444toNV12

GPU-Z GPU-Z _ _

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

GPU-Z -GPU-Z(GPU ) -PC

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. CPU-Z, GPU-Z, Core Temp, SpeedFan (Area-51 R2) CPU-Z and GPU-Z are great for showing you data about your Alienware hardware. Below are the shots from Area-51 R2 CPU-Z, GPU-Z, Core Temp, SpeedFan (Area-51 R2) CPU-Z and GPU-Z are great for showing you data about your Alienware hardware. Below are the shots from Area-51 R2 (2025). Adding the free Core Temp and SpeedFan

CPU-Z, GPU-Z, Core Temp, SpeedFan (Area-51 R2) - Dell

2011/01/16 18:31:48Location: Louisville, KY Status: offline Ribbons : 1 Re:My First Precision Skin 2012/01/31 09:35:03 (permalink) Thanks for the info...can't wait to try it tonight! Win 7 64-bit - Corsair Obsidian Series 800D with Extended Water Cooling Case - Intel Core I7 2700k @ 4.40GHz - Asus Maximus IV Extreme-Z - 16G's G.Skill DDR3-2133 11-11-11-13 1.6v - Corsair HX1000i 80+ Platinum PSU - 2 EVGA GTX 780's in SLI with Hydro Copper-1 Quad & 1 Triple Rad with 2 Swiftech - MCP655 Pumps 1 Bitpower 150 Reservoir - 1 Bitpower 250 Reservoir - 1 Swiftech Apogee for CPU - 4 Intel SSD 320 Sata II 120Gb in Raid 0 - 3 Acer G235h Monitors - 1 Asus VG248QE Logitech G910 Keyboard - Logitech G502 Laser Mouse - Creative Sound Blaster Z & Sennheiser PC Headset 350 SE - 5.1 Panasonic A/V Receiver - Polk Audio Speakers & Sub Woofer www.jeffgoatley.com/mymodrig Sparax New Member Total Posts : 24 Reward points : 0 Joined: 2008/02/26 16:21:44Location: UK Status: offline Ribbons : 0 Re:My First Precision Skin 2012/01/31 09:51:39 (permalink) No probs mate glad i could help BTW nice rig you've got there i bet that cost a small fortune CPU : Intel Q9450 @ 3.6GHz MOB : Asus Striker II Extreme 790i Ultra SLI MEM : 2x2GB Corsair Dominator GT (CMT4GX3M2A2000C8) GPU : NVIDIA GeForce GTX 560 Ti (2048 MB) APU : X-Fi Titanium Fatal1ty PSU : CoolerMaster 850W Modular HDD : 1 x 120GB Corsair Force 3 SSD + 1 x 2TB WD2002FAEX + 2 x 1TB WD1001FALS jeffgoatley iCX Member Total Posts : 280 Reward points : 0 Joined: 2011/01/16 18:31:48Location: Louisville, KY Status: offline Ribbons : 1 Re:My First Precision Skin 2012/01/31 09:55:47 (permalink) Thanks...just updated to 1155 and the 2700k. I'll be adding soon. Win 7 64-bit - Corsair Obsidian Series 800D with Extended Water Cooling Case - Intel Core I7 2700k @ 4.40GHz - Asus Maximus IV Extreme-Z - 16G's G.Skill DDR3-2133 11-11-11-13 1.6v - Corsair HX1000i 80+ Platinum PSU - 2 EVGA GTX 780's in SLI with Hydro Copper-1 Quad & 1 Triple Rad with 2 Swiftech - MCP655 Pumps 1 Bitpower 150 Reservoir - 1 Bitpower 250 Reservoir - 1 Swiftech Apogee for CPU - 4 Intel SSD 320 Sata II 120Gb in Raid 0 - 3 Acer G235h Monitors - 1 Asus VG248QE Logitech G910 Keyboard - Logitech G502 Laser Mouse - Creative Sound

Area-51 R2, CPU-Z, GPU-Z, Core Temp, SpeedFan - Dell

XP-Pen 15.6 .. OS: Windows 10 Pro Unwinder Superclocked Member Total Posts : 214 Reward points : 0 Joined: 2007/06/05 09:27:52 Status: offline Ribbons : 12 Re:EVGA Precision X Custom Skins 2013/12/13 04:20:54 (permalink) vernacularEverything should be aligned properly, however I've ran into an issue where the sync indicator doesn't appear. It should make the normally-white circles around each GPU number on the GPU list bold and green. I've tried using the sync indicator images from the default skin as well as changing all positions back to default, but the sync indicators still don't appear for me. I also tried setting the Z-position of all the objects in that area and although it DID have an effect on the visible objects the sync indicator objects still don't appear. The password to decompile is the default password for all EVGA Precision skins if someone else wants to look into it. PM me if you figure it out.To provide backward compatibility with old skins GPU5 - GPU8 buttons and GPU sync indicators are only being read from the skin if the skin defines GPU page button. Your BUTTON_GPU_PAGE is not defined properly causing it to be ignored by skin engine and causing the skin to be treated as old format (with 4 GPU buttons only and without GPU sync indicators). Drerex CLASSIFIED Member Total Posts : 3511 Reward points : 0 Joined: 2007/10/26 16:29:58Location: St. Augustine, FL Status: offline Ribbons : 31 Re:EVGA Precision X Custom Skins 2013/12/13 07:12:12 (permalink) Unwinder is the man vernacular. He was the one that gave me the original code plus is the root creator. vernacular Superclocked Member Total Posts : 187 Reward points : 0 Joined: 2008/06/19 19:47:46Location: US Status: offline Ribbons : 3 Re:EVGA Precision X Custom Skins 2013/12/14 02:27:51 (permalink) Yes! It works! Thanks, Unwinder. I was getting really annoyed trying to figure that out.I've updated the post with skin & link. CASE: Lian Li O11-Dynamic (Alpine White) .. MOBO: GIGABYTE Z390 Aorus Master .. CPU: Intel i9-9900K w/ EK Supremacy EVO Nickel .. GPU: EVGA 2080 Ti XC Gaming w/ Phanteks Glacier Waterblock

gpu-z -gpu-z v2.41.0 -

- [cpu time: avg 0.118 ms, total 1107.61 ms] [avg calls per frame: 2]00:06:51: encoder thread frame - [cpu time: avg 0.123 ms, total 592.803 ms] [avg calls per frame: 1]00:06:51: ==============================================================00:06:51: 00:06:51: =====Stream End: 2014-01-07, 00:06:51=================================================00:08:26: Settings::Video: Disabling Aero00:24:38: Settings::Video: Disabling Aero07:01:38: =====Stream Start: 2014-01-07, 07:01:38===============================================07:01:38: Multithreaded optimizations: On07:01:38: Base resolution: 1920x108007:01:38: Output resolution: 1920x108007:01:38: ------------------------------------------07:01:38: Loading up D3D10 on NVIDIA GeForce GTX 670 (Adapter 1)...07:01:38: ------------------------------------------07:01:38: Audio Format: 44100hz07:01:38: Playback device {0.0.0.00000000}.{ec4f4ef7-b694-4e2d-a768-32ca47b2f4dc}07:01:38: ------------------------------------------07:01:38: Using desktop audio input: Speakers (2- ASUS Xonar DX Audio Device)07:01:38: ------------------------------------------07:01:38: Using auxilary audio input: Microphone (2- ASUS Xonar DX Audio Device)07:01:38: ------------------------------------------07:01:38: Audio Encoding: AAC07:01:38: bitrate: 12807:01:38: Using Window Capture07:01:38: Scene buffering time set to 70007:01:38: ------------------------------------------07:01:38: Video Encoding: x26407:01:38: fps: 3007:01:38: width: 1920, height: 108007:01:38: preset: veryfast07:01:38: profile: main07:01:38: keyint: 6007:01:38: CBR: yes07:01:38: CFR: yes07:01:38: max bitrate: 350007:01:38: buffer size: 350007:01:38: ------------------------------------------07:01:38: MMDeviceAudioSource: Frequency for device 'Speakers (2- ASUS Xonar DX Audio Device)' is 352800, samples per sec is 4410007:01:38: MMDeviceAudioSource: Frequency for device 'Microphone (2- ASUS Xonar DX Audio Device)' is 352800, samples per sec is 4410007:05:37: Total frames encoded: 7150, total frames duplicated: 0 (0.00%)07:05:37: Total frames rendered: 7155, number of late frames: 1 (0.01%) (it's okay for some frames to be late)07:05:37: 07:05:37: Profiler time results:07:05:37: 07:05:37: ==============================================================07:05:37: video thread frame - [100%] [avg time: 0.72 ms] [children: 53.8%] [unaccounted: 46.3%]07:05:37: | scene->Preprocess - [47.6%] [avg time: 0.343 ms]07:05:37: | GPU download and conversion - [6.11%] [avg time: 0.044 ms] [children: 5.42%] [unaccounted: 0.694%]07:05:37: | | flush - [3.33%] [avg time: 0.024 ms]07:05:37: | | CopyResource - [1.81%] [avg time: 0.013 ms]07:05:37: | | conversion to 4:2:0 - [0.278%] [avg time: 0.002 ms]07:05:37: Convert444Threads - [100%] [avg time: 0.981 ms] [children: 99.6%] [unaccounted: 0.408%]07:05:37: | Convert444toNV12 - [99.6%] [avg time: 0.977 ms]07:05:37: encoder thread frame - [100%] [avg time: 1.618 ms]07:05:37: ==============================================================07:05:37: 07:05:37: 07:05:37: Profiler CPU results:07:05:37: 07:05:37: ==============================================================07:05:37: video thread frame - [cpu time: avg 0.015 ms, total 109.2 ms] [avg calls per frame: 1]07:05:37: | scene->Preprocess - [cpu time: avg 0 ms, total 0 ms] [avg calls per frame: 1]07:05:37: | GPU download and conversion - [cpu time: avg 0.002 ms, total 15.6 ms] [avg calls per frame: 1]07:05:37: | | flush - [cpu time: avg 0.002 ms, total 15.6 ms] [avg calls per frame: 1]07:05:37: | | CopyResource - [cpu time: avg 0 ms, total 0 ms] [avg calls per frame: 1]07:05:37: | | conversion to. CPU-Z, GPU-Z, Core Temp, SpeedFan (Area-51 R2) CPU-Z and GPU-Z are great for showing you data about your Alienware hardware. Below are the shots from Area-51 R2 CPU-Z, GPU-Z, Core Temp, SpeedFan (Area-51 R2) CPU-Z and GPU-Z are great for showing you data about your Alienware hardware. Below are the shots from Area-51 R2 (2025). Adding the free Core Temp and SpeedFan