1. 1. CPU, GPU, GPGPU Architecture
+CPU, GPU, and GPGPU architectures are all types of computer processing +architectures, but they differ in their design and operation.
+CPU: A central processor (CPU) is a processing unit that is designed to +perform various computing tasks including data processing, mathematical +and logical calculations, communication between different components of +a computer system, etc. Modern CPUs usually have multiple cores to +process multiple tasks simultaneously.
+GPU: A graphics processing unit (GPU) is an architecture designed to +accelerate the processing of images and graphics. GPUs have thousands of +cores that allow them to process millions of pixels simultaneously, +making them an ideal choice for video games, 3D modeling, and other +graphics-intensive applications.
+GPGPU: A General Processing Architecture (GPGPU) is a type of GPU that +is designed to be used for purposes other than graphics processing. +GPGPUs are used to perform computations of an intensive nature using the +hundreds or thousands of cores available on the graphics card. They are +particularly effective for parallel computing, machine learning, and +other computationally intensive areas.
+In conclusion, the main difference between the three architectures CPU, +GPU and GPGPU lies in their design and operation. While CPUs are +designed for general computer processing, GPUs are designed for +specialized graphics processing, and GPGPUs are a modified version of +GPUs intended to be used for specialized computer processing other than +graphics processing.
+1.1. 1.1 CPU
+The CPU basically consists of three parts:
+-
+
-
+
The control unit which searches for instructions in +memory, decodes them and coordinates the rest of the processor to +execute them. A basic control unit basically consists of an instruction +register and a "decoder/sequencer" unit
+
+ -
+
The Arithmetic and Logic Unit executes the +arithmetic and logic instructions requested by the control unit. +Instructions can relate to one or more operands. The execution speed is +optimal when the operands are located in the registers rather than in +the memory external to the processor.
+
+ -
+
Registers are memory cells internal to the CPU +They are few in number but very quick to access. They +are used to store variables, the intermediate results of operations +(arithmetic or logical) or processor control information.
+
+
+
The register structure varies from processor to processor. This is why +each type of CPU has its own instruction set. Their basic functions are nevertheless similar and all processors have roughly the same categories of registers:
+-
+
-
+
The accumulator is primarily intended to hold the data that needs to +be processed by the ALU.
+
+ -
+
General registers* are used to store temporary data and intermediate
+
+ -
+
Address registers* are used to construct particular data addresses. +These are, for example, the base and index registers which allow, among +other things, to organize the data in memory like indexed tables.
+
+ -
+
The instruction register contains the code of the instruction which is processed by the decoder/sequencer.
+
+ -
+
The ordinal counter contains the address of the next instruction to be executed. In principle, this register never stops counting. It generates the addresses of the instructions to be executed one after the other. Some instructions sometimes require changing the contents of the ordinal counter to make a sequence break, ie a jump elsewhere in the program.
+
+ -
+
The status register, sometimes called the condition register, +contains indicators called flags whose values (0 or 1) vary according +to the results of the arithmetic and logical operations. These states +are used by conditional jump instructions.
+
+
The stack pointer or stack pointer manages certain data in memory by +organizing them in the form of stacks.
+CPU working principle
+The content of the program counter is deposited on the addressing bus in +order to search there for a machine code instruction. The control bus +produces a read signal and the memory, which is selected by the address, +sends the instruction code back to the processor via the data bus. Once +the instruction lands in the instruction register, the processor’s +control unit decodes it and produces the appropriate sequence of +internal and external signals that coordinate its execution. An +instruction comprises a series of elementary tasks. They are clocked by +clock cycles.
+All the tasks that constitute an instruction are executed one after the +other. The execution of an instruction therefore lasts several cycles. +As it is not always possible to increase the frequency, the only way to +increase the number of instructions processed in a given time is to seek +to execute several of them simultaneously. This is achieved by splitting +processor resources, data and/or processes. This is called the +parallelization.
+The different architectures of the processor
+There is a classification of the different CPU architectures. Five in +number, they are used by programmers depending on the desired results:
+-
+
-
+
+++
CISC: very complex addressing;
+
+ -
+
+++
RISC: simpler addressing and instructions performed on a single cycle;
+
+ -
+
+++
VLIW: long, but simpler instructions;
+
+ -
+
+++
vectorial: contrary to the processing in number, the instructions are +vectorial;
+
+ -
+
+++
dataflow: data is active unlike other architectures.
+
+
To further improve the performance of this processor, developers can +add so-called SIMD Supplemental Instruction Sets.
+1. 2 GPU (Graphics Processing Unit is a graphics (co-)processor)
+Graphics Processing Unit is a graphics (co-)processor capable of very +efficiently performing calculations on images (2D, 3D, videos, etc.). +The raw computing power offered is higher due to the large number of +processors present on these cards. This is why it is not uncommon to +obtain large acceleration factors between CPU and GPU for the same +application.
+Explicit code targeting GPUs: CUDA, HIP, SYCL, Kokkos, RAJA,…
++
Fig: illustrates the main hardware architecture differences between +CPUs and GPUs. The transistor counts associated with various functions +are represented abstractly by the relative sizes of the various shaded +areas. In the figure, the green corresponds to the calculation; gold is +instruction processing; purple is the L1 cache; blue is top level cache +and orange is memory (DRAM, which really should be thousands of times +larger than caches).
+GPUs were originally designed to render graphics. They work great for +shading, texturing, and rendering the thousands of independent polygons +that make up a 3D object. CPUs, on the other hand, are meant to control +the logical flow of any general-purpose program, where a lot of digit +manipulation may (or may not) be involved. Due to these very different +roles, GPUs are characterized by having many more processing units and +higher overall memory bandwidth, while CPUs offer more sophisticated +instruction processing and faster clock speed.
++ | CPU: Latency-oriented design | +GPU: Throughput Oriented Design | +
---|---|---|
Clock |
+High clock frequency |
+Moderate clock frequency |
+
Caches |
+
+
+Large sizes +
+ Converts high latency accesses in memory to low latency accesses in +cache + |
+
+
+Small caches +
+ To maximize memory throughput + |
+
Control |
+
+
+Sophisticated control system +
+ Branch prediction to reduce latency due to branching |
+
+
+Single controlled +
+
+No branch prediction +
+ No data loading + |
+
Powerful Arithmetic Logic Unit (ALU) |
+Reduced operation latency |
+Numerous, high latency but heavily pipelined for high throughput |
+
Other aspects |
+
+
+Lots of space devoted to caching and control logic. Multi-level caches +used to avoid latency +
+
+Limited number of registers due to fewer active threads +
+ Control logic to reorganize execution, provide ILP, and minimize +pipeline hangs + |
+Requires a very large number of threads for latency to be tolerable |
+
Beneficial aspects for applications |
+
+
+CPUs for sequential games where latency is critical. +
+ CPUs can be 10+X faster than GPUs for sequential code. + |
+
+
+GPUs for parallel parts where throughput is critical. +
+ GPUs can be 10+X faster than GPUs for parallel code. + |
+
1.3 GPGPU ( General-Purpose Graphics Processing Unit)
++
A General-Purpose Graphics Processing Unit (GPGPU) is a graphics +processing unit (GPU) that is programmed for purposes beyond graphics +processing, such as performing computations typically conducted by a +Central Processing Unit (CPU).
+GPGPU is short for general-purpose computing on graphics processing +units. Graphics processors or GPUs today are capable of much more than +calculating pixels in video games. For this, Nvidia has been developing +for four years a hardware interface and a programming language derived +from C, CUDA ( C ompute Unified Device Architecture ). This +technology, known as GPGPU ( General - P urpose computation on G +raphic P rocessing Units ) exploits the computing power of GPUs for +the processing of massively parallel tasks. Unlike the CPU, a GPU is not +suited for fast processing of tasks that run sequentially. On the other +hand, it is very suitable for processing parallelizable algorithms.
+•Array of independent "cores" called calculation units
+-
+
-
+
High bandwidth, banked L2 caches and main memory
+
+
− Banks allow several parallel accesses
+− 100s of GB/s
+-
+
-
+
Memory and caches are generally inconsistent
+
+
Compute units are based on SIMD hardware
+− Both AMD and NVIDIA have 16-element wide SIMDs
+-
+
-
+
Large registry files are used for fast context switching
+
+
− No save/restore state
+− Data is persistent throughout the execution of the thread
+-
+
-
+
Both providers have a combination of automatic L1 cache and +user-managed scratchpad
+
+ -
+
Scratchpad is heavily loaded and has very high bandwidth +(~terabytes/second)
+
+
Work items are automatically grouped into hardware threads called +"wavefronts" (AMD) or "warps" (NVIDIA)
+− Single instruction stream executed on SIMD hardware
+− 64 work items in a wavefront, 32 in a string
+-
+
-
+
The instruction is issued multiple times on the 16-channel SIMD unit
+
+ -
+
Control flow is managed by masking the SIMD channel
+
+
NVIDIA coined "Single Instruction Multiple Threads" (SIMT) to refer to +multiple (software) threads sharing a stream of instructions
+-
+
-
+
Work items run in sequence on SIMD hardware
+
+
− Multiple software threads are executed on a single hardware thread
+− Divergence between managed threads using predication
+-
+
-
+
Accuracy is transparent to the OpenCL model
+
+ -
+
Performance is highly dependent on understanding work items to SIMD +mapping
+
+
1.4 Architecture of a GPU versus CPU
+Such an architecture is said to be "throughput-oriented". The latest +from the Santa-Clara firm, codenamed “Fermi” has 512 cores.
++
CPU architecture vs. GPUs
+Traditional microprocessors (CPUs) are essentially "low latency +oriented". The goal is to minimize the execution time of a single +sequence of a program by reducing latency as much as possible. This +design takes the traditional assumption that parallelism in the +operations that the processor must perform is very rare.
+Throughput-oriented processors assume that their workload requires +significant parallelism. The idea is not to execute the operations as +quickly as possible sequentially, but to execute billions of operations +simultaneously in a given time, the execution time of one of these +operations is ultimately almost irrelevant. In a video game, for +example, performance is measured in FPS (Frames Per Seconds). To do +this, an image, with all the pixels, must be displayed every 30 +milliseconds (approximately). It doesn’t matter how long a single pixel +is displayed.
+This type of processor has small independent calculation units which +execute the instructions in the order in which they appear in the +program, there is ultimately little dynamic control over the execution. +Thea term SIMD is used for these processors (Single Instruction Multiple Data).
+Each PU (Processing Unit) does not necessarily correspond to a +processor, they are calculation units. In this mode, the same +instruction is applied simultaneously to several data.
+Less control logic means more space on the chip dedicated to the +calculation. However, this also comes at a cost. A SIMD execution gets a +performance peak when parallel tasks follow the same branch of +execution, which deteriorates when the tasks branch off. Indeed, the +calculation units assigned to a branch will have to wait for the +execution of the calculation units of the previous branch. This results +in hardware underutilization and increased execution time. The +efficiency of the SIMD architecture depends on the uniformity of the +workload.
+However, due to the large number of computational units, it may not be +very important to have some threads blocked if others can continue their +execution. Long-latency operations performed on one thread are "hidden" +by others ready to execute another set of instructions.
+For a quad or octo-core CPU, the creation of threads and their +scheduling has a cost. For a GPU, the relative latency "covers" these 2 +steps, making them negligible. However, memory transfers have greater +implications for a GPU than a CPU because of the need to move data +between CPU memory and GPU memory.
+SIMD (Single Instruction Multiple Data)
+SIMD is a computer technique that allows several data elements to be +exploited at the same time.
+What is SIMD used for?
+SIMD can be used in a wide range of applications, such as 3D graphics, +signal processing, data mining, and many other processing-intensive +tasks. In the realm of 3D graphics, SIMD can be used to process large +amounts of data in parallel, making graphics rendering faster and +smoother. In signal processing, SIMD can be used to process multiple +signals at the same time, thereby increasing the efficiency of signal +processing. In data mining, SIMD can be used to process large volumes of +data in parallel, which makes data mining faster and more efficient.
+SIMD is also commonly used in encryption and data compression +algorithms. These algorithms often require the processing of large +amounts of data, and SIMD can be used to speed up the process. SIMD can +also be used to process large amounts of data in parallel in machine +learning algorithms such as artificial neural networks.
+Benefits of using SIMD
+SIMD has several advantages over other forms of parallelization. First, +SIMD is more efficient than traditional software parallelization +techniques, such as threading. This is because SIMD takes advantage of +the capabilities of modern processors and is optimized for parallelism. +This means that SIMD can process multiple pieces of data in parallel at +the same time, which greatly improves program performance.
+In addition, SIMD allows more efficient use of memory. Since the same +instruction is applied to multiple pieces of data in parallel, the +amount of memory required to store data is reduced. This can help +improve performance by reducing the amount of memory required to store +data items.
+Finally, SIMD is more flexible than other forms of parallelization. This +is because SIMD allows the same instruction to be applied to multiple +data items in parallel, allowing the programmer to customize the code +according to application requirements.
+1.5 AMD ROCm Platform, CUDA
+1.5.1 AMD ROC platform
+ROCm™ is a collection of drivers , development tools, and APIs that +enable GPU programming from low-level kernel to end-user applications +. ROCm is powered by AMD’s Heterogeneous Computing Interface for +Portability , an OSS C++ GPU programming environment and its +corresponding runtime environment . HIP enables ROCm developers to +build portable applications across different platforms by deploying code +on a range of platforms , from dedicated gaming GPUs to exascale HPC +clusters .
+ROCm supports programming models such as OpenMP and OpenCL , and +includes all necessary compilers , debuggers and OSS libraries . ROCm +is fully integrated with ML frameworks such as PyTorch and TensorFlow +. ROCm can be deployed in several ways , including through the use of +containers such as Docker , Spack, and your own build from source .
+ROCm is designed to help develop , test, and deploy GPU-accelerated HPC +, AI , scientific computing , CAD, and other applications in a free , +open-source , integrated, and secure software ecosystem .
+CUDA Platform
+CUDA® is a parallel computing platform and programming model developed +by NVIDIA for general computing on graphics processing units (GPUs). +With CUDA, developers can dramatically speed up computing applications +by harnessing the power of GPUs.
+The CUDA architecture is based on a three-level hierarchy of cores, +threads, and blocks. Cores are the basic unit of computation while +threads are the individual pieces of work that the cores work on. Blocks +are collections of threads that are grouped together and can be run +together. This architecture enables efficient use of GPU resources and +makes it possible to run multiple applications at once.
+The NVIDIA CUDA-X platform, which is built on CUDA®, brings together a +collection of libraries, tools, and technologies that deliver +significantly higher performance than competing solutions in multiple +application areas ranging from artificial intelligence to high +performance computing.
+GPUs | ++ |
---|---|
CUDA ( Compute Unified Device Architecture) |
+HIP +("Heterogeneous-Compute Interface for Portability") |
+
+
+Has been the de facto standard for native GPU code for years +
+
+Huge set of optimized libraries available +
+
+Custom syntax (extension of C++) supported only by CUDA compilers +
+ Support for NVIDIA devices only + |
+
+
+AMD’s effort to offer a common programming interface that works on both +CUDA and ROCm devices +
+
+Standard C++ syntax, uses the nvcc/hcc compiler in the background +
+
+Almost an individual CUDA clone from the user’s perspective +
+ The ecosystem is new and growing rapidly + |
+
1.5.3 What is the difference between CUDA and ROCm for GPGPU +applications?
+NVIDIA’s CUDA and AMD’s ROCm provide frameworks to take advantage of the +respective GPU platforms.
+Graphics processing units (GPUs) are traditionally designed to handle +graphics computing tasks, such as image and video processing and +rendering, 2D and 3D graphics, vectorization, etc. General purpose +computing on GPUs became more practical and popular after 2001, with the +advent of programmable shaders and floating point support on graphics +processors.
+Notably, it involved problems with matrices and vectors, including two-, +three-, or four-dimensional vectors. These were easily translated to +GPU, which acts with native speed and support on these types. A +milestone for general purpose GPUs (GPGPUs) was the year 2003, when a +pair of research groups independently discovered GPU-based approaches +for solving general linear algebra problems on working GPUs faster than +on CPUs.
+1.6 GPGPU Evolution
+Early efforts to use GPUs as general-purpose processors required +reframing computational problems in terms of graphics primitives, which +were supported by two major APIs for graphics processors: OpenGL and +DirectX.
+These were soon followed by NVIDIA’s CUDA, which allowed programmers to +abandon underlying graphics concepts for more common high-performance +computing concepts, such as OpenCL and other high-end frameworks. This +meant that modern GPGPU pipelines could take advantage of the speed of a +GPU without requiring a complete and explicit conversion of the data to +a graphical form.
+NVIDIA describes CUDA as a parallel computing platform and application +programming interface (API) that allows software to use specific GPUs +for general-purpose processing. CUDA is a software layer that provides +direct access to the GPU’s virtual instruction set and parallel +computing elements for running compute cores.
+Not to be outdone, AMD launched its own general-purpose computing +platform in 2016, dubbed the Radeon Open Compute Ecosystem (ROCm). ROCm +is primarily intended for discrete professional GPUs, such as AMD’s +Radeon Pro line. However, official support is more extensive and extends +to consumer products, including gaming GPUs.
+Unlike CUDA, the ROCm software stack can take advantage of multiple +areas, such as general-purpose GPGPU, high-performance computing (HPC), +and heterogeneous computing. It also offers several programming models, +such as HIP (GPU kernel-based programming), OpenMP/Message Passing +Interface (MPI), and OpenCL. These also support microarchitectures, +including RDNA and CDNA, for a myriad of applications ranging from AI +and edge computing to IoT/IIoT.
+NVIDIA’s CUDA
+Most of NVIDIA’s Tesla and RTX series cards come with a series of CUDA +cores designed to perform multiple calculations at the same time. These +cores are similar to CPU cores, but they are integrated into the GPU and +can process data in parallel. There can be thousands of these cores +embedded in the GPU, making for incredibly efficient parallel systems +capable of offloading CPU-centric tasks directly to the GPU.
+Parallel computing is described as the process of breaking down larger +problems into smaller, independent parts that can be executed +simultaneously by multiple processors communicating through shared +memory. These are then combined at the end as part of an overall +algorithm. The primary purpose of parallel computing is to increase +available computing power to speed up application processing and problem +solving.
+To this end, the CUDA architecture is designed to work with programming +languages such as C, C++ and Fortran, allowing parallel programmers to +more easily utilize GPU resources. This contrasts with previous APIs +such as Direct3D and OpenGL, which required advanced graphics +programming skills. CUDA-powered GPUs also support programming +frameworks such as OpenMP, OpenACC, OpenCL, and HIP by compiling this +code on CUDA.
+As with most APIs, software development kits (SDKs), and software +stacks, NVIDIA provides libraries, compiler directives, and extensions +for the popular programming languages mentioned earlier, making +programming easier and more effective. These include cuSPARCE, NVRTC +runtime compilation, GameWorks Physx, MIG multi-instance GPU support, +cuBLAS and many more.
+A good portion of these software stacks are designed to handle AI-based +applications, including machine learning and deep learning, computer +vision, conversational AI, and recommender systems.
+Computer vision applications use deep learning to acquire knowledge from +digital images and videos. Conversational AI applications help computers +understand and communicate through natural language. Recommender systems +use a user’s images, language, and interests to deliver meaningful and +relevant search results and services.
+GPU-accelerated deep learning frameworks provide a level of flexibility +to design and train custom neural networks and provide interfaces for +commonly used programming languages. All major deep learning frameworks, +such as TensorFlow, PyTorch, and others, are already GPU-accelerated, so +data scientists and researchers can upgrade without GPU programming.
+Current use of the CUDA architecture that goes beyond AI includes +bioinformatics, distributed computing, simulations, molecular dynamics, +medical analytics (CTI, MRI and other scanning imaging applications ), +encryption, etc.
+AMD’s ROCm Software Stack
+AMD’s ROCm software stack is similar to the CUDA platform, except it’s +open source and uses the company’s GPUs to speed up computational tasks. +The latest Radeon Pro W6000 and RX6000 series cards are equipped with +compute cores, ray accelerators (ray tracing) and stream processors that +take advantage of RDNA architecture for parallel processing, including +GPGPU, HPC, HIP (CUDA-like programming model), MPI and OpenCL.
+Since the ROCm ecosystem is composed of open technologies, including +frameworks (TensorFlow/PyTorch), libraries (MIOpen/Blas/RCCL), +programming models (HIP), interconnects (OCD), and support upstream +Linux kernel load, the platform is regularly optimized. for performance +and efficiency across a wide range of programming languages.
+AMD’s ROCm is designed to scale, meaning it supports multi-GPU computing +in and out of server-node communication via Remote Direct Memory Access +(RDMA), which offers the ability to directly access host memory without +CPU intervention. Thus, the more RAM the system has, the greater the +processing loads that can be handled by ROCm.
+ROCm also simplifies the stack when the driver directly integrates +support for RDMA peer synchronization, making application development +easier. Additionally, it includes ROCr System Runtime, which is language +independent and leverages the HAS (Heterogeneous System Architecture) +Runtime API, providing a foundation for running programming languages +such as HIP and OpenMP.
+As with CUDA, ROCm is an ideal solution for AI applications, as some +deep learning frameworks already support a ROCm backend (e.g. +TensorFlow, PyTorch, MXNet, ONNX, CuPy, etc.). According to AMD, any +CPU/GPU vendor can take advantage of ROCm, as it is not a proprietary +technology. This means that code written in CUDA or another platform can +be ported to vendor-neutral HIP format, and from there users can compile +code for the ROCm platform.
+The company offers a series of libraries, add-ons and extensions to +deepen the functionality of ROCm, including a solution (HCC) for the C++ +programming language that allows users to integrate CPU and GPU in a +single file.
+The feature set for ROCm is extensive and incorporates multi-GPU support +for coarse-grained virtual memory, the ability to handle concurrency and +preemption, HSA and atomic signals, DMA and queues in user mode. It also +offers standardized loader and code object formats, dynamic and offline +compilation support, P2P multi-GPU operation with RDMA support, event +tracking and collection API, as well as APIs and system management +tools. On top of that, there is a growing third-party ecosystem that +bundles custom ROCm distributions for a given application across a host +of Linux flavors.
+To further enhance the capability of exascale systems, AMD also +announced the availability of its open source platform, AMD ROCm, which +enables researchers to harness the power of AMD Instinct accelerators +and drive scientific discovery. Built on the foundation of portability, +the ROCm platform is capable of supporting environments from multiple +vendors and accelerator architectures.
+And with ROCm5.0, AMD extends its open platform powering the best HPC +and AI applications with AMD Instinct MI200 series accelerators, +increasing ROCm accessibility for developers and delivering +industry-leading performance on workloads keys. And with AMD Infinity +Hub, researchers, data scientists, and end users can easily find, +download, and install containerized HPC applications and ML frameworks +optimized and supported on AMD Instinct and ROCm.
+The hub currently offers a range of containers supporting Radeon +Instinct™ MI50, AMD Instinct™ MI100, or AMD Instinct MI200 accelerators, +including several applications such as Chroma, CP2k, LAMMPS, NAMD, +OpenMM, etc., as well as frameworks Popular TensorFlow and PyTorch MLs. +New containers are continually being added to the hub.
+3. Moves to Unify CPUs and GPUs
++
1.7 TPU (Tensor Processing Unit) form Google
+A Tensor Processing Unit (TPU) is a specialized hardware processor +developed by Google to accelerate machine learning. Unlike traditional +CPUs or GPUs, TPUs are specifically designed to handle tensor +operations, which account for most of the computations in deep learning +models. This makes them incredibly efficient at those tasks and provides +an enormous speedup compared to CPUs and GPUs. In this article, we’ll +explore what a TPU is, how it works, and why they are so beneficial for +machine learning applications.
+What Are Tensor Processing Units (TPU)?
+Tensor Processing Unit (TPU) is an application-specific integrated +circuit (ASIC) designed specifically for machine learning. In addition, +TPUs offer improved energy efficiency, allowing businesses to reduce +their electricity bills while still achieving the same results as +processors with greater energy consumption. This makes them an +attractive option for companies looking to use AI in their products or +services. With the help of TPUs, businesses can develop and deploy +faster, more efficient models that are better suited to their needs. +TPUs offer a range of advantages over CPUs and GPUs. For instance, +they provide up to 30x faster performsance than traditional processors +and up to 15x better energy efficiency. This makes them ideal for +companies looking to develop complex models in a fraction of the +time. Finally, TPUs are more affordable than other specialized +hardware solutions, making them an attractive option for businesses of +all sizes.
+Tensor Processing Units are Google’s ASIC for machine learning. TPUs are +specifically used for deep learning to solve complex matrix and vector +operations. TPUs are streamlined to solve matrix and vector operations +at ultra-high speeds but must be paired with a CPU to give and execute +instructions.
++
Applications for TPUs
+TPUs can be used in various deep learning applications such as fraud +detection, computer vision, natural language processing, self-driving +cars, vocal AI, agriculture, virtual assistants, stock trading, +e-commerce, and various social predictions.s
+When to Use TPUss
+Since TPUs are high specialized hardware for deep learning, it loses a +lot of other functions you would typically expect from a general-purpose +processor like a CPU. With this in mind, there are specific scenarios +where using TPUs will yield the best result when training AI. The best +time to use a TPU is for operations where models rely heavily on matrix +computations, like recommendation systems for search engines. TPUs also +yield great results for models where the AI analyzes massive amounts of +data points that will take multiple weeks or months to complete. AI +engineers use TPUs for instances without custom TensorFlow models and +have to start from scratch.
+When Not to Use TPUs
+As stated earlier, the optimization of TPUs causes these types of +processors to only work on specific workload operations. Therefore, +there are instances where opting to use a traditional CPU and GPU will +yield faster results. These instances include:
+-
+
-
+
Rapid prototyping with maximum flexibility
+
+ -
+
Models limited by the available data points
+
+ -
+
Models that are simple and can be trained quickly
+
+ -
+
Models too onerous to change
+
+ -
+
Models reliant on custom TensorFlow operations written in C++
+
+
TPU Versions and Specifications | ++ |
---|---|
TPUv1 |
+The first publicly announced TPU. Designed as an 8-bit matrix +multiplication engine and is limited to solving only integers. |
+
TPUv2: |
+Since engineers noted that TPUv1 was limited in bandwidth. This +version now has double the memory bandwidth with 16GB of RAM. This +version can now solve floating points making it useful for training and +inferencing. |
+
TPUv3 |
+Released in 2018, TPUv3 has twice the processors and is deployed +with four times as many chips as TPUv2. The upgrades allow this version +to have eight times the performance over previous versions. |
+
TPUv4 |
+This is the latest version of TPU announced on May 18, 2021. +Google’s CEO announced that this version would have more than twice the +performance of TPU v3. |
+
Edge TPU |
+This TPU version is meant for smaller operations optimized to +use less power than other versions of TPU in overall operation. Although +only using two watts of power, Edge TPU can solve up to four +terra-operations per second. Edge TPU is only found on small handheld +devices like Google’s Pixel 4 smartphone. |
+
Benefits of the TPU Architecture | ++ |
---|---|
High Performance: |
+The TPU architecture is designed to maximize +performance, ensuring that the processor can execute operations at +extremely high speeds. |
+
Low Power Consumption: |
+Compared to CPUs and GPUs, the TPU architecture +requires significantly less power consumption, making it ideal for +applications in which energy efficiency is a priority. |
+
Cost Savings: |
+The TPU architecture is designed to be affordable, +making it an attractive solution for businesses that are looking to +reduce their hardware costs. |
+
Scalability |
+The TPU architecture is highly scalable and can +accommodate a wide range of workloads, from small applications to +large-scale projects. |
+
Flexibility |
+The TPU architecture is flexible and can be adapted to +meet the needs of different applications, making it suitable for a range +of use cases. |
+
Efficient Training |
+The TPU architecture enables efficient training of +deep learning models, allowing businesses to quickly iterate and improve +their AI solutions. |
+
Security |
+The TPU architecture is highly secure, making it an ideal +solution for mission-critical applications that require high levels of +security. |
+
Enhanced Reliability |
+The TPU architecture has enhanced reliability, +providing businesses with the assurance that their hardware will perform +as expected in any environment. |
+
Easy to Deploy |
+The TPU architecture is designed for easy deployment, +allowing businesses to quickly set up and deploy their hardware +solutions. |
+
Open Source Support |
+The TPU architecture is backed by an open-source +community that provides support and assistance when needed, making it +easier for businesses to get the most out of their hardware investments. |
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Improved Efficiency |
+The TPU architecture is designed to optimize +efficiency, allowing businesses to get the most out of their hardware +resources and reducing the cost of running AI applications. |
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End-to-End Solutions: |
+The TPU architecture provides a complete +end-to-end solution for all types of AI projects, allowing businesses to +focus on their development and operations instead of worrying about +hardware compatibility. |
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Cross-Platform Support |
+The TPU architecture is designed to work across +multiple platforms, making it easier for businesses to deploy their AI +solutions in any environment. |
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Future Ready |
+The TPU architecture is designed with the future in mind, +providing businesses with a solution that will remain up-to-date and +ready to take on next-generation AI applications. |
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Industry Standard |
+The TPU architecture is becoming an industry +standard for AI applications, giving businesses the confidence that +their hardware investments are future-proofed. |
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Applications of the TPU
+Tensor Processing Units (TPUs) are specialized ASIC chips designed to +accelerate the performance of machine learning algorithms. They can be +used in a variety of applications, ranging from cloud computing and edge +computing to machine learning. TPUs provide an efficient way to process +data, making them suitable for a range of tasks such as image +recognition, language processing, and speech recognition. By leveraging +the power of TPUs, organizations can reduce costs and optimize their +operations.
+Cloud Computing: TPUs are used in cloud computing to provide better +performance for workloads that require a lot of data processing. This +allows businesses to process large amounts of data quickly and +accurately at a lower cost than ever before. With the help of TPUs, +businesses can make more informed decisions faster and improve their +operational efficiency.
+Edge Computing: TPUs are also used in edge computing applications, +which involve processing data at or near the source. This helps to +reduce latency and improve performance for tasks such as streaming audio +or video, autonomous driving, robotic navigation, and predictive +analytics. Edge computing also facilitates faster and more reliable +communication between devices in an IoT network.
+Machine Learning: TPUs are used to accelerate machine learning models +and algorithms. They can be used to develop novel architectures that are +optimized for tasks such as natural language processing, image +recognition, and speech recognition. By leveraging the power of TPUs, +organizations can develop more complex models and algorithms faster. +This will enable them to achieve better results with their +machine-learning applications.
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