A 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.
HPC 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, 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.
Latency vs Throughput
Let’s first take a look at the main differences between a Central Processing Unit (CPU) and a GPU. A common CPU is optimized to be 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.
Differences and Similarities
However, 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. (more…)
GPU (Graphics Processor Unit) ni kifaa cha vifaa kinachotumiwa wakati wa kuendesha programu ambazo zina uzito mkubwa kwenye picha. Programu ya 3D ya kuunda mifano ya VDI Katika soko la watumiaji, GPU ni hasa kutumika kuharakisha graphics michezo ya kubahatisha. Leo, GPGPU (General Purpose GPU) ni chaguo la vifaa vya kuharakisha mzigo wa kazi ya hesabu katika mazingira ya kisasa ya High Performance Computing (HPC). HPC ni jukwaa la kazi ya kazi kama vile Machine Learning (ML), Deep Learning (DL), na Artificial Intelligence (AI). GPGPU ni moja ya njia za msingi za utambuzi wa picha. Mahesabu juu ya data ya jedwali pia ni zoezi la kawaida katika i.e. Usalama, Bima na Viwanda vya Fedha Kwa nini GPU inahitajika kwa kazi hii? Katika makala hii, tutazungumzia juu ya GPU na kwa nini ni muhimu kwa kazi za HPC zinazoendesha kwenye vSphere ESXi. Latency vs Throughput Hebu kwanza kuangalia tofauti kuu kati ya Kituo cha Utaratibu (CPU) na GPU. CPU ya kawaida imeboreshwa kuwa haraka iwezekanavyo kumaliza kazi kwa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda wa muda. Ni asili ni wote kuhusu usindikaji kazi katika njia serialized. GPU ni wote kuhusu throughput optimization, kuruhusu kushinikiza kazi nyingi iwezekanavyo kwa njia ya ni ndani kwa wakati mmoja. Inafanya hivyo kwa kuwa na uwezo wa mchakato sambamba kazi. Mfano zifuatazo mchoro inaonyesha <unk>core<unk> idadi ya CPU na GPU. Ni muhimu kutambua kwamba tofauti kuu kati ya GPU na GPU ni kwamba GPU ina nyuklia nyingi zaidi za kuendesha kazi. Tofauti na Kufanana Hata hivyo, si tu kuhusu idadi ya cores. Kwa mfano, katika NVIDIA GPU, kuna msingi wa CUDA, ambayo ni pamoja na ALU (Arithmetic Logic Unit). Maneno yanaweza kutofautiana kati ya wauzaji. (Zaidi...)