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| NVIDIA Tesla (microarchitecture) | |
|---|---|
| Name | NVIDIA Tesla (microarchitecture) |
| Designer | NVIDIA |
| Architecture | Tesla |
| Produced start | 2006 |
| Produced end | 2008 |
| Process | 90 nm, 65 nm |
| Cores | up to 240 (GeForce 8800 GTX variants) |
| Predecessor | NV40 |
| Successor | GT200 |
NVIDIA Tesla (microarchitecture) NVIDIA Tesla (microarchitecture) is a GPU microarchitecture developed by NVIDIA introduced with the GeForce 8800 family in 2006. It unified graphics and general-purpose computation concepts, influencing CUDA, GPGPU adoption, and high-performance computing in supercomputers and data centers. Tesla's design informed later products across GeForce, Quadro, and Tesla (brand) lines and intersected with industry trends led by companies like AMD and initiatives such as OpenCL.
Tesla marked a shift from previous fixed-function pipelines to a fully programmable shader architecture, debuting in the GeForce 8 series including GeForce 8800 GTX, GeForce 8800 GTS, and GeForce 8600 variants. The microarchitecture emphasized massively parallel scalar processors, stream processing, and a unified shader model, aligning NVIDIA with trends visible in devices from Intel and IBM and standards promulgated by the Khronos Group. Tesla's arrival affected software ecosystems like Microsoft DirectX 10 and OpenGL 2.0 and spurred research at institutions such as MIT, Stanford University, and UC Berkeley into GPU-accelerated computing.
Tesla introduced a scalar single-instruction multiple-thread (SIMT) design with execution units organized into streaming multiprocessors, memory hierarchies featuring global and local caches, and a unified register file. The architecture contained multiple streaming multiprocessors with many stream processors derived from vector-scalar tradeoffs studied at NVIDIA Research and in collaboration with academic groups including University of Illinois Urbana–Champaign and Carnegie Mellon University. Memory subsystem features, influenced by designs from ATI Technologies and research from Lawrence Berkeley National Laboratory, included GDDR3 memory controllers, high-bandwidth memory interfaces, texture units, and support for Z-buffer and antialiasing operations used in 3D graphics workloads like those found in titles from Electronic Arts and Activision.
Tesla's instruction set extended previous NVIDIA GPU ISAs to support general-purpose programming models and exposed capabilities later formalized in CUDA and hardware mappings used in frameworks such as OpenCL and libraries like cuBLAS and cuFFT. The programming model allowed developers from organizations including NVIDIA Developer Zone, research groups at Lawrence Livermore National Laboratory, and companies like Google and Facebook to port parallel algorithms, leveraging thread blocks, warps, and shared memory concepts. Compiler toolchains, debug tools, and profilers evolved in ecosystems alongside compilers from GCC-based projects and proprietary tools from NVIDIA Nsight.
Tesla delivered large improvements in shader throughput and rasterization performance compared to predecessors, enabling higher frame rates in benchmarks from publications such as PC Magazine, AnandTech, and Tom's Hardware. Its parallelism scaled across multiple GPUs via technologies similar to SLI and influenced multi-GPU configurations in workstation and HPC cluster environments, including deployments in national labs like Oak Ridge National Laboratory and commercial centers such as Amazon Web Services. Performance characteristics were evaluated using suites from SPEC, media benchmarks in Adobe products, and scientific workloads in molecular dynamics and finite element analysis used by researchers at Harvard University.
Tesla underpinned consumer, professional, and HPC product lines: mainstream GeForce 8800 cards, professional Quadro FX variants, and early Tesla-branded accelerators for compute-focused customers at companies like IBM and Dell. System integrators built blade servers and workstations using Tesla-derived GPUs for applications in computer graphics, visualization for studios like Pixar and Industrial Light & Magic, and compute tasks in bioinformatics groups at Broad Institute. OEMs such as HP and Lenovo offered systems featuring Tesla-based GPUs in their workstation product catalogs.
Contemporaneous reviews from outlets including CNET, PC Gamer, and The New York Times praised Tesla for transformative performance per watt and programmability, while analysts at firms like Gartner and IDC noted its role in accelerating the GPU compute market. Tesla catalyzed academic and industry research into accelerating traditional CPU workloads with GPUs, influencing projects at NASA, CERN, and universities exploring accelerated simulation and machine learning precursors that later contributed to work at Google Brain and startups like Nervana Systems.
Tesla's concepts were extended and refined in successors such as GT200 and later architectures including Fermi (microarchitecture), Kepler (microarchitecture), and Pascal (microarchitecture), shaping the development of modern accelerators in AI and deep learning research enclaves like Stanford AI Lab and corporate labs at Microsoft Research. Its legacy persists in contemporary products used across high-performance computing centers, cloud platforms from Microsoft Azure and Google Cloud Platform, and in standards bodies including the Khronos Group that continue to evolve GPU programming interfaces.
Category:Graphics processing units