CPUs as they have thousands of cores for number calculations. GPUs in addition use less energy per computation than CPUs. Every computer, smartphone and tablet has GPUs in it.


7.3.1 GPU-Accelerated Computing


GPU-accelerated computing is the use of a GPU together with a CPU to accelerate applications, offering increased performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From the perspective of the user, the application simply runs significantly faster. CPU-GPU collaboration is necessary to achieve high-


performance computing. Known as heterogeneous computing (HC), which intelligently combines the best features of both CPU and GPU to achieve high computational gains, it aims to match the requirements of each application to the strengths of CPU/GPU architectures and also achieve load-balancing by avoiding idle time for both the processing units. Novel optimisation techniques are required to fully realise the potential of HC and to move towards the goals of exascale performance. A simple way to understand the difference between a CPU


and GPU is to compare how they process tasks. A CPU consists of a few cores optimised for sequential serial processing, while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling


multiple tasks simultaneously. Solving a computational problem on a GPU is in principle


similar to solving a problem using many CPUs. Te task at hand must be split into small tasks where each task is performed by a single GPU core. Communication between the GPU cores is handled by internal registers and memory on the GPU chip. Instead of programming using message passing, special programming languages like CUDA or OpenCL provide mechanisms for data exchange between the host CPU and synchronising the GPU cores. A modern supercomputer system may then in practice


consist of a large number of nodes, each holding between 2 and 32 conventional CPUs as well as 1–4 GPUs. Tere will usually also be a high-speed network and a system for data storage. Te software for this system can be written using a combination of conventional programming languages, like C or C++, combined with a message passing system for parallelisation of the CPUs and in addition CUDA or OpenCL for the GPUs. All of these components must be tuned and optimised for best possible performance of the total system. Te second fastest GPU-enabled supercomputer on the


TOP500 list, where it is number four, is the Titan Supercomputer at Oak Ridge National Lab (see Figure 7.6). Fitted with 299,088 CPU cores and 18,688 NVIDIA 2880-core Tesla K20 GPU accelerators, it achieved 17.59 PFlop/s on the Linpack benchmark. Titan is the first major supercomputing system to utilise a hybrid


Figure 7.11: The Piz Daint supercomputer is the flagship for the Swiss National Supercomputing Centre. Piz Daint is the most powerful system in Europe, having computing power of 19.6 PFlops, or 19.6 quadrillion mathematical operations per second. The supercomputer provides users with two types of compute nodes: hybrid CPU-GPU and CPU-only nodes. Compared to a CPU, the GPU has reduced functionalities that are optimised for numerical calculations, which enables the GPU to compute much faster, while saving energy. Piz Daint is the second most energy efficient system in the TOP500 list, consuming a total of 2.72 MW and delivering 10.398 Gflops/W. Following tradition, CSCS named the supercomputer after a Swiss mountain, ‘Piz Daint’, in the Alps.


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