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Rtx 3090 fp645/16/2023 ![]() ![]() Both caches are used to store data in local and global memory, including register spilling. The 768 KB L2 cache is unified and shared among all SMs that services all operations (load, store and texture). Frequent accesses to a cached L1-memory location does not increase the probability of hitting the datum, but it is attractive when several threads are accessing to adjacent memory spaces. Whereas the CPU L1 cache is designed for spatial and temporal locality, the GPU L1 is only optimized for spatial locality. It introduces 64 KB of configurable shared memory and an L1 cache per SM, which can be configured as 16 KB of L1 cache with 48 KB of shared memory or 16 KB of shared memory with 48 KB of L1 cache. Each SM has two warp schedulers which enable issue and execute 2 warps concurrently.Ī key block of this architecture is the memory hierarchy. In order to execute double precision, the 32 CUDA cores can perform as 16 FP64 units. Each CUDA core has a fully pipelined arithmetic logic unit (ALU) as well as a floating point unit (FPU). The CPU is connected to the GPU via a PCI-e bus. The board has six 64-bit memory partitions with a 384-bit memory interface which supports up to 6 GB of GDDR5 DRAM memory. Fermi ArchitectureĮach Fermi Streaming Multiprocessor (SMI) is composed of 32 CUDA cores (Streaming Processors), 16 load/store units (LD/ST units) to address memory operations for sixteen threads per clock, four special function units (SFU) to execute transcendental mathematical instructions, a memory hierarchy and warp schedulers.įermi Streaming Multiprocessor ( Image by author) Tesla is a quite simple architecture, so I decided to start with Fermi directly, which introduces Error-Correcting Code memory, really improves the context switching, the memory hierarchy and the double precision. ![]() Finally, the Jetson line contains embedded GPUs in chips.Īs we just saw above, Nvidia started its adventure in the early 90s with GPUs focused on grapichs, but we waited until 2007 to work with the first CUDA architecture: Tesla (yes, you are right, they used the same name of the architecture for a product line later, that’s why I said it can be confusing). The GeForce line is focused on desktop and gamers the Quadro is thought to workstations and developers who create video-content whereas Tesla is designed for supercomputers and HPC. In Nvidia words, they four have the same compute capability. Traditionally, Nvidia has designed a different type of product for each target category of clients, given the name to four different products: GeForce, Quadro, Tesla and (more recently) Jetson although the underlying architecture used internally is the same for the four products. It is interesting to point out the difference between target categories and architectures in the Nvidia world, which can be confusing for the readers. However, the GeForce 3 was likely the first popular Nvidia GPU. ![]() ![]() But before going into further details, I strongly recommend you to visit my previous post about the CUDA execution model, if you are not familiar with GPU computing.įollowing the natural timeline of Nvidia GPUs, the company first produced a chip capable of programmable shading in 2001, the GeForce 3, used by the Playstation 2 and the Xbox. In the following paragraphs, I will describe a global overview of the CUDA architectures from beginning to end today, let’s drive the interesting road from Fermi to Ampere together. Ampere is the last architecture of our favorite GPU brand, but several generations of CUDA-capable GPUs has been released so far. Nvidia just announced its Geforce RTX 30 Series (RTX3090, RTX3080, RTX3070), based on the Ampere architecture. Today is the turn of HPC, specifically we are talking about GPUs advances. So, any progress on any of these fields will help Machine Learning to grow. Someone defined Machine learning as the perfect harmony among maths (algorithms), engineering (High Performance Computing) and human hability (experience). ![]()
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