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Adaptive scalable texture compression (ASTC) is a lossy block-based texture compression algorithm developed by Jørn Nystad et al. of ARM Ltd. and AMD. [ 1 ] Full details of ASTC were first presented publicly at the High Performance Graphics 2012 conference, in a paper by Olson et al. entitled "Adaptive Scalable Texture Compression".
This number is generally used as a maximum throughput number for the GPU and generally, a higher fill rate corresponds to a more powerful (and faster) GPU. Memory subsection. Bandwidth – Maximum theoretical bandwidth for the processor at factory clock with factory bus width. GHz = 10 9 Hz. Bus type – Type of memory bus or buses used.
Texture (GT/s) Type Size Bandwidth (GB/s) Bus width Clock Bfloat16 Single precision Double precision; Data Center GPU Max 1100: Jan 10, 2023: Xe-HPC (Ponte Vecchio) Multiple [75] 100 1280 7168:448:0:56:448:448 28 MB 204 MB 1000 1550: 0 448.0 694.4: HBM2E: 48 GB 1228.8 3072-bit 3200 352: 14.336 22.221: 300 W PCIe 5.0 x16 Data Center GPU Max 1350 ...
A similar concept, texture fillrate, refers to the number of texture map elements the GPU can map to pixels in one second. Texture fillrate is obtained by multiplying the number of texture mapping units (TMUs) by the clock frequency of the GPU. Texture fillrates are given in mega or gigatexels per second.
One of the first graphics cards to support DirectX 9.0 natively was ATI's Radeon 9700, though the effect wasn't programmed into games for years afterwards. On August 23, 2003, Microsoft updated DirectX to DirectX 9.0b, which enabled the Pixel Shader 2.x (Extended) profile for ATI's Radeon X series and NVIDIA's GeForce FX series of graphics ...
TechPowerUp GPU-Z (or just GPU-Z) is a lightweight utility designed to provide information about video cards and GPUs. [2] The program displays the specifications of Graphics Processing Unit (often shortened to GPU) and its memory; also displays temperature, core frequency, memory frequency, GPU load and fan speeds.
GPU usage by codec – some codecs can drastically increase their performance by taking advantage of GPU resources. So, for example, codec A (being optimized for memory usage – i.e., uses less memory) may, on modern computers (which are typically not memory-limited), give slower performance than codec B.
Notably, problems involving matrices and/or vectors – especially two-, three-, or four-dimensional vectors – were easy to translate to a GPU, which acts with native speed and support on those types. A significant milestone for GPGPU was the year 2003 when two research groups independently discovered GPU-based approaches for the solution of ...
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