Sep 07, · One more thing that you might have to consider is the spacing between PCI-e slots. As RTX cards are usually of size > PCIe slot. So I do have apprehensions about Gigabyte Aorus Master being able to fit in 3 cards at the same time without PCI-e extenders. Also currently a large majority of PCI-e extenders are The German University in Cairo, GUC, is an Egyptian Private University - founded by Prof. Dr. Ashraf Mansour - established in cooperation with the State Universities of Ulm and Stuttgart, under the patronage of the Egyptian Ministry of Higher Education, the Ministry of Science, Research and Arts, State of Baden-Wuerttemberg, Germany, and supported by the German Academic Exchange Service 1. Introduction. Property valuation, also known as real estate appraisal, plays a fundamental role in a nation's economy and financial stability. According to Taffese [], financial and economic decisions are based on the accuracy of property valuation blogger.com example, the housing market bubbles can cause serious financial risks such as the subprime mortgage crisis in the Great Recession
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Deep learning is a field with intense computational requirements, and your choice of GPU will fundamentally determine your deep learning experience. But what features are important if you want to buy a new GPU? GPU RAM, cores, tensor cores? How to make a cost-efficient choice? This blog post will delve into these questions, tackle common misconceptions, give you an intuitive understanding of how to think about GPUs, and will lend you advice, which will help you to make a choice that is right for you.
This blog post is designed to give you different levels of understanding of GPUs and the new Master thesis repository eur series GPUs from NVIDIA. You have the choice: 1 If you are not interested in the details of how GPUs work, what makes a GPU fast, and what is unique about the new NVIDIA RTX 30 Ampere series, you can skip right to the performance and performance per dollar charts and the recommendation section.
These form the core of the blog post and the most valuable content. You might want to skip a section or two based on your understanding of the presented topics. I will head each major section with a small summary, which might help you to decide if you want to read the section or not.
This blog post is structured in the following way. First, I will explain what makes a GPU fast. I will discuss CPUs vs GPUs, Tensor Cores, memory bandwidth, and the memory hierarchy of GPUs and how these relate to deep learning performance. These explanations might help you get a more intuitive sense of what to look for in a GPU. Then I will make theoretical estimates for GPU performance and master thesis repository eur them with some marketing benchmarks from NVIDIA to get reliable, unbiased performance data.
I discuss the unique features of the new NVIDIA RTX 30 Ampere GPU series that are worth considering if you buy a GPU. From there, I make GPU recommendations for4, master thesis repository eur, 8 GPU setups, and GPU clusters. If you use GPUs frequently, it is useful to understand how they work. This knowledge will come in handy in understanding why GPUs might be slow in some cases and fast in others.
In turn, you might be able to understand better why you need a GPU in master thesis repository eur first place and how other future hardware options might be able to compete. You can skip this section if you just want the useful performance numbers and arguments to help you decide which GPU to buy. The best high-level explanation for the question of how GPUs work is my following Quora answer:.
This is a high-level explanation that explains quite well why GPUs are better than CPUs for deep learning. If we look at the details, we can understand what makes one GPU better than another. This section can help you build a more intuitive understanding of how to think about deep learning performance.
This understanding will help you to evaluate future GPUs by yourself. There are now enough cheap GPUs that almost everyone can afford a GPU with Tensor Cores. That master thesis repository eur why I only recommend GPUs with Tensor Cores.
It is useful to understand how they work to appreciate the importance of these computational units specialized for matrix multiplication. This is a simplified example, and not the exact way how a high performing matrix multiplication kernel would be written, but it has all the basics. To understand this example fully, you have to understand the concepts of cycles. Each cycle represents an opportunity for computation.
However, most of the time, operations take longer than one cycle. Thus it creates a pipeline where for one operation to start, it needs to wait for the number of cycles of time it takes for the previous operation to finish, master thesis repository eur. Master thesis repository eur is also called the latency of the operation. Furthermore, you should know that the smallest units of threads on a GPU is a pack of 32 threads — this is called a warp.
Warps usually operate in a synchronous pattern — threads within a warp have to wait for each other, master thesis repository eur. All memory operations on the GPU are optimized for warps.
The resources master thesis repository eur an SM are divided up among all active warps. For both of the following examples, we assume we have the same computational resources. A memory block in shared memory is often referred to as a memory tile or just a tile.
We have 8 SMs with 8 warps each, so due to parallelization, we only need to do a single sequential load from global to shared memory, which takes cycles. To do the matrix multiplication, we now need to load a vector of 32 numbers from shared memory A and shared memory B and perform a fused multiply-and-accumulate FFMA. Then store the outputs in registers C. We divide the work so that each SM does 8x dot products 32×32 to compute 8 outputs of C. Why this is exactly 8 4 in older algorithms is very technical.
This means we have 8x shared memory access at the cost of 20 cycles each and 8 FFMA operations 32 in parallelwhich cost 4 cycles each. In total, we thus have a cost of:.
With Tensor Cores, we can perform a 4×4 matrix multiplication in one cycle. To do that, we first need to get memory into the Tensor Core, master thesis repository eur.
Similarly to the above, we need to read from global memory cycles and store in shared memory. A single SM has 8 Tensor Cores. So with 8 SMs, we have 64 Tensor Cores — just the number that we need! We can transfer the data from shared memory to the Tensor Cores with 1 memory transfers 20 cycles and then do those 64 parallel Tensor Core operations 1 cycle.
This means the total cost for Tensor Cores matrix multiplication, in this case, is:. Thus we reduce the matrix multiplication cost significantly from cycles to cycles via Tensor Cores. In this simplified case, the Tensor Cores reduced the cost of both shared memory access and FFMA operations.
While this example roughly follows the sequence of computational steps for both with and without Tensor Cores, please note that this is a very simplified example. Real cases of matrix multiplication involve much larger shared memory tiles and slightly different computational patterns. However, I believe from this example, it is also clear why the next attribute, memory bandwidth, is so crucial for Tensor-Core-equipped GPUs.
Since global memory is the most considerable portion of cycle cost for master thesis repository eur multiplication with Tensor Cores, we would even have faster GPUs if the global memory latency could be reduced, master thesis repository eur.
We can do this by either increasing the clock frequency of the memory more cycles per second, but also more heat and higher energy requirements or by increasing the number of elements that can be transferred at any one time bus width. From the previous section, we have seen that Tensor Cores are very fast.
So fast, in fact, that they are idle most of the time as they are waiting for memory to arrive from global memory. Since memory transfers to the Tensor Cores are the limiting factor in performance, master thesis repository eur, we are looking for other GPU attributes that enable faster memory transfer to Tensor Cores. Shared memory, L1 Cache, and amount of registers used are all related.
To understand how a memory hierarchy enables faster memory transfers, it helps to understand how matrix multiplication is performed on a GPU. To perform matrix multiplication, we exploit the memory hierarchy of a GPU that goes from slow global memory to fast local shared memory, to lightning-fast registers. However, the faster the memory, the master thesis repository eur it is. As such, we need to separate the matrix into smaller matrices, master thesis repository eur.
We perform matrix multiplication across these smaller tiles in local shared memory that is fast and close to the streaming multiprocessor SM — the equivalent of a CPU core.
With Tensor Cores, we go a step further: We take each tile and load a part of these tiles into Tensor Cores. Having larger tiles means we can reuse more memory. I wrote about this in detail in my TPU vs GPU blog post. In fact, you can see TPUs as having very, very, large tiles for each Tensor Core. As such, TPUs can reuse much more memory with each transfer from global memory, which makes them a little bit more efficient at matrix multiplications than GPUs.
We have the following shared memory sizes on the following architectures:. We see that Ampere has a much larger shared memory allowing for larger tile sizes, which reduces global memory access. Thus, Ampere can make better use of the overall memory bandwidth on the GPU memory. The performance boost is particularly pronounced for huge matrices. The Ampere Tensor Cores have another advantage in that they master thesis repository eur more data between threads, master thesis repository eur.
This reduces the register usage. Registers are limited to 64k per streaming multiprocessor SM or per thread. Comparing the Volta vs Ampere Tensor Core, the Ampere Tensor Core uses 3x fewer master thesis repository eur, allowing for more tensor cores to be active for each shared memory tile. In other words, we can feed 3x as many Tensor Cores with the same amount of registers. However, since bandwidth is still the bottleneck, you will only see tiny increases in actual vs theoretical TFLOPS.
Overall, you can see that the Ampere architecture is optimized to make the available memory bandwidth more effective by using an improved memory hierarchy: from global memory to shared memory tiles, to register tiles for Tensor Cores. This section is for those who want to understand the more technical details of how I derive the performance estimates for Ampere GPUs. If you do not care about these technical aspects, it is safe to skip this section.
Putting together the reasoning above, we would expect the difference between two Tensor-Core-equipped GPU architectures to be mostly about memory bandwidth. This puts the speedup range between 1. With similar reasoning, master thesis repository eur, you would be able to estimate the speedup of other Ampere series GPUs compared to a Tesla V Suppose we have an estimate for one GPU of a GPU-architecture like Ampere, Turing, or Volta.
Luckily, NVIDIA already benchmarked the A vs V across a wide range of computer vision and natural language understanding tasks. Unfortunately, NVIDIA made sure that these numbers are not directly comparable by using different batch sizes and the number of GPUs whenever possible to favor results for the A So in a sense, the benchmark numbers are partially honest, partially marketing numbers.
In general, you could argue that using larger batch sizes is fair, as the A has more memory. Still, to compare GPU architectures, we should evaluate unbiased memory performance with the same batch size. To get an unbiased estimate, we can scale the V and A results in two ways: 1 account for the differences in batch size, 2 account for the differences in using 1 vs 8 GPUs.
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Sep 07, · One more thing that you might have to consider is the spacing between PCI-e slots. As RTX cards are usually of size > PCIe slot. So I do have apprehensions about Gigabyte Aorus Master being able to fit in 3 cards at the same time without PCI-e extenders. Also currently a large majority of PCI-e extenders are Apr 27, · Doctoral thesis demonstrated that relying on native language is the most successful strategy when learning idioms On 15 June at 10 AM Rita Anita Forssten, doctoral student at the School of Humanities in Tallinn University, is defending her doctoral thesis “The effects of L1-L2 analogy and transparency in Est 1. Introduction. Property valuation, also known as real estate appraisal, plays a fundamental role in a nation's economy and financial stability. According to Taffese [], financial and economic decisions are based on the accuracy of property valuation blogger.com example, the housing market bubbles can cause serious financial risks such as the subprime mortgage crisis in the Great Recession
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