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The document compares memory and cache concepts between CPU and GPU architectures, including cache layers and global memory access, and describes how GPUs mask memory latency through abundant concurrent computations. It also connects the architecture to HPC workloads such as machine learning, deep learning, and artificial intelligence on vSphere ESXi.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/exploring-the-gpu-architecture-overview-to-conclude/656164/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/exploring-the-gpu-architecture-overview-to-conclude/656164.png","ImageObject",300,407,{"name":92,"@type":93},"Bintang","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-10-25",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main difference between CPU and GPU optimization goals?","Question",{"text":112,"@type":113},"CPUs are optimized for finishing tasks with low latency and quick switching between operations, while GPUs focus on maximizing throughput by parallel processing many tasks at once.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How do GPUs handle memory latency compared with CPUs?",{"text":117,"@type":113},"GPUs are tolerant of memory latency and mask potential delays by keeping enough computations running in parallel so the device remains busy.",{"name":119,"@type":110,"acceptedAnswer":120},"Why are GPUs suitable for HPC workloads like AI and machine learning?",{"text":121,"@type":113},"Modern HPC workloads involve computationally intensive tasks such as machine learning, deep learning, and artificial intelligence, and GPUs provide data-parallel throughput that accelerates these workloads effectively.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},656164,1793071095,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},962085564381,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Exploring the GPU Architecture  \nVMware AI/ML  \n © VMware LLC.  \nExploring the GPU Architecture  \nTable of contents  \nExploring the GPU Architecture .................................................................................................................... 3  \nOverview ................................................................................................................................................... 3  \nLatency vs Throughput ............................................................................................................................... 4  \nDifferences and Similarities ......................................................................................................................... 5  \nExploring the GPU Architecture .................................................................................................................... 6  \nTo Conclude ............................................................................................................................................... 8  \n © VMware LLC.  \nExploring the GPU Architecture  \nExploring the GPU Architecture Overview  \nA Graphics Processor Unit (GPU) is mostly known for the hardware device used when running applications that weigh heavy on graphics, i.e. 3D modeling software or VDI infrastructures. In the consumer market, a GPU is mostly used to accelerate gaming graphics. Today, GPGPU’s (General Purpose GPU) are the choice of hardware to accelerate computational workloads in modern High Performance Computing (HPC) landscapes.  \nHPC in itself is the platform serving workloads like Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI) . Using a GPGPU is not only about ML computations that require image recognition anymore. Calculations on tabular data is also a common exercise in [i.e. healthcare](i.e. healthcare), insurance and financial industry verticals. But why do we need a GPU for these types of all these workloads? This blogpost will go into the GPU architecture and why they are a good fit for HPC workloads running on vSphere ESXi.  \n © VMware LLC. Document | 3  \nExploring the GPU Architecture  \nLatency vs Throughput  \nLet’s first take a look at the main differences between a Central Processing Unit (CPU) and a GPU. A common CPU is optimized tobe as quick as possible to finish a task at a as low as possible latency, while keeping the ability to quickly switch between operations. It’s nature is all about processing tasks in a serialized way. A GPU is all about throughput optimization, allowing to push as many as possible tasks through is internals at once. It does so by being able to parallel process a task. The following exemplary diagram shows the ‘core’ count of a CPU and GPU. It emphasizes that the main contrast between both is that a GPU has a lot more cores to process a task.  \n© VMware LLC. Document | 4  \nExploring the GPU Architecture  \nDifferences and Similarities  \nHowever, it is not only about the number of cores. And when we speak of cores in a NVIDIA GPU, we refer to CUDA cores that consists of ALU’s (Arithmetic Logic Unit) . Terminology may vary between vendors.  \nLooking at the overall architecture of a CPU and GPU, we can see a lot of similarities between the two. Both use the memory constructs of cache layers, memory controller and global memory. A high-level overview of modern CPU architectures indicates it is all about low latency memory access by using significant cache memory layers. Let’s first take a look at a diagram that shows an generic, memory focussed, modern CPU package (note: the precise lay-out strongly depends on vendor/model) .  \nA single CPU package consists of cores that contains separate data and instruction layer-1 caches, supported by the layer-2 cache. The layer-3 cache, or last level cache, is shared across multiple cores. If data is not residing in the cache layers, it will fetch the data from the global DDR-4 memory. The numbers of cores per CPU can go up to 28 ","cbCaikQg2Ss4biqL","https://ap.wps.com/l/cbCaikQg2Ss4biqL","pdf",291528,"English","# Exploring the GPU Architecture\n## Overview\n## Latency vs Throughput\n## Differences and Similarities\n## To Conclude","[{\"question\":\"What is the main difference between CPU and GPU optimization goals?\",\"answer\":\"CPUs are optimized for finishing tasks with low latency and quick switching between operations, while GPUs focus on maximizing throughput by parallel processing many tasks at once.\"},{\"question\":\"How do GPUs handle memory latency compared with CPUs?\",\"answer\":\"GPUs are tolerant of memory latency and mask potential delays by keeping enough computations running in parallel so the device remains busy.\"},{\"question\":\"Why are GPUs suitable for HPC workloads like AI and machine learning?\",\"answer\":\"Modern HPC workloads involve computationally intensive tasks such as machine learning, deep learning, and artificial intelligence, and GPUs provide data-parallel throughput that accelerates these workloads effectively.\"}]","Exploring the GPU Architecture - Overview - To Conclude | PDF",1792966818,23]