On the latest NVIDIA A100, Tesla V100, Tesla T4, Tesla P100, and Quadro GV100/GP100 GPUs, ECC support is included in the main HBM2 memory, as well as in register files, shared memories, L1 cache and L2 cache.
NVIDIA Tesla GPUs are able to correct single-bit errors and detect & alert on double-bit errors. Such issues are not uncommon – our technicians regularly encounter memory errors on consumer gaming GPUs. It is up to the user to detect errors (whether they cause application crashes, obviously incorrect data, or subtly incorrect data). Neither the GPU nor the system can alert the user to errors should they occur. Titan GPUs do not include error correction or error detection capabilities. For others, a single-bit error may not be so easy to detect (returning incorrect results which appear reasonable). For some applications, a single error can cause the simulation to be grossly and obviously incorrect. However, technical computing applications rely on the accuracy of the data returned by the GPU. The user is very unlikely to even be aware of the issue. On a GPU running a computer game, one memory error typically causes no issues (e.g., one pixel color might be incorrect for one frame). *** Value given with and without Sparsity feature Error Detection and Correction We consider it very poor scientific methodology to compare performance between varied precisions however, we also recognize a desire to see at least an order of magnitude performance comparison between the Deep Learning performance of diverse generations of GPUs. For reference, we are providing the maximum known deep learning performance at any precision if there is no TensorFLOPS value. Tensor Cores are only available on “Volta” GPUs or newer. NVIDIA is now measuring GPUs with Tensor Cores by a new deep learning performance metric: a new unit called TensorTFLOPS.
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It combines a multiply of two FP16 units (into a full precision product) with a FP32 accumulate operation-the exact operations used in Deep Learning Training computation. ** Value is estimated and calculated based upon theoretical FLOPS (clock speeds x cores) TensorFLOPS and Deep Learning PerformanceĪ new, specialized Tensor Core unit was introduced with “Volta” generation GPUs. * Exact value depends upon PCI-Express or SXM2 SKU Half-precision (16-bit) Floating Point Performance Here is a comparison of the half-precision floating-point calculation performance between GeForce and Tesla/Quadro GPUs: NVIDIA GPU Model Although all NVIDIA “Pascal” and later GPU generations support FP16, performance is significantly lower on many gaming-focused GPUs. This was previously the standard for Deep Learning/AI computation however, Deep Learning workloads have moved on to more complex operations (see TensorCores below). Support for half-precision FP16 operations was introduced in the “Pascal” generation of GPUs. Some applications do not require as high an accuracy (e.g., neural network training/inference and certain HPC uses). * Exact value depends upon PCI-Express or SXM2 SKU FP16 16-bit (Half Precision) Floating Point Calculations
Here is a comparison of the double-precision floating-point calculation performance between GeForce and Tesla/Quadro GPUs: NVIDIA GPU Modelĭouble-precision (64-bit) Floating Point Performance Although almost all NVIDIA GPU products support both single- and double-precision calculations, the performance for double-precision values is significantly lower on most consumer-level GeForce GPUs. Less accurate values are called single-precision (32-bit). These larger values are called double-precision (64-bit). In these applications, data is represented by values that are twice as large (using 64 binary bits instead of 32 bits). Many applications require higher-accuracy mathematical calculations. FP64 64-bit (Double Precision) Floating Point Calculations
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There are many features only available on the professional Datacenter, RTX Professional, and Tesla GPUs. However, it’s wise to keep in mind the differences between the products. The consumer line of GeForce and RTX Consumer GPUs may be attractive to some running GPU-accelerated applications.
All NVIDIA GPUs support general purpose computation (GPGPU), but not all GPUs offer the same performance or support the same features. This resource was prepared by Microway from data provided by NVIDIA and trusted media sources.
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