Find our more about our company on these pages. Frankfurt am Main, July 28, Continental is accelerating the development of future technologies with a supercomputer that is unrivaled in the automotive industry. This is required for the development of pioneering future technologies in assisted, automated and autonomous driving, for example. And by doing so, the technology company is underlining its core competencies in software and networking as well as in the architecture of systems.
These have been working together in a data center in Frankfurt am Main since early The new supercomputer from Continental is ranked according to the current list of TOP supercomputers as the top system in the automotive industry.
We use these to simulate real-life, physical test drives — and need fewer journeys actually on the road as a result. By doing so, we are significantly reducing the time required for programming, including the training of artificial neural networks. Without supercomputers, several thousand hours of training involving millions of images and therefore enormous amounts of data are necessary to train a neural network.
The high-performance computer now reduces the time needed for this process, taking it down from weeks to hours. Managing complex driving scenarios is one of the biggest challenges on the way towards autonomous mobility. More than 20, of the more than 51, engineers working for Continental are experts in the areas of software and IT. Continental already employs more than experts in AI, and by , it will have about 1, Value creation with software is recording double-digit percentage growth each year.
A single customer alone will have received more than 2. This puts Continental among the front-running companies in the industry. A particularly high-performance vehicle server was recently developed for Volkswagen. By , Continental expects more than ten further projects.
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Product Finder. Corporate Strategy. Corporate Governance. For Suppliers. Continental Magazine. Back Press. Select Country. Select Language. Corporate Topics. Vehicle Types. Quarterly Publications. Schwartz et al. Therefore, reducing the energy consumption is the other major factor that drives us to port and to refactor the model to new architectures.
While we have stated multiple benefits for redesigning the models for new supercomputer platforms, the challenge, especially in the coding parts, is also manyfold. The first major challenge comes from the heavy burden of the legacy climate code Fu et al. The second major challenge comes from the computation load pattern of the code. Unlike an earthquake model with large hotspots You et al.
The third challenge comes from the dramatic change in architecture. With the GPU devices introduced as accelerators for general-purpose computing around Owens et al. With both a high parallelism and a high arithmetic density, these modules demonstrate a high speedup ranging from 10 to 70 times or even up to times when migrating from CPU to GPU.
In contrast, the dynamical core code, which involves both time-stepping integration and communication among different modules is more challenging to port to heterogeneous accelerators. Examples include NIM Govett et al. For migrating an entire model, the efforts are even fewer in number. For the porting of a model at such a level, the three challenges mentioned above heavy burden of legacy code, hundreds of hotspots distributed through the code, and the mismatch between the existing code and the emerging hardware have apparently combined to produce more challenges.
Facing the problem of tens of thousands of lines of code, the researchers and developers have to either perform an extensive rewriting of the code Xu et al. With the early-stage efforts focusing on the atmospheric component CAM5 Fu et al. This requires better accuracy and better conservation of matter and energy so as to perform simulation of hundreds of years instead of just hundreds of days; although, as the first step, we are not changing the original algorithm design to minimize the uncertainties of code and results.
Although our current work targets a single system with a specifically designed Sunway processor and does not consider performance portability, our successful first experiences provide encouraging references for general scientific modelling on heterogeneous many-core machines such as GPU-based high-performance computing HPC systems, and this work potentially opens the door for further addressing performance portability in the future.
The rest of the paper is organized as follows. Section 2 describes the major features of the Sunway TaihuLight supercomputer, including its architecture, energy consumption details, the corresponding parallelization strategies, etc. Next, Sect. Section 4 demonstrates stable and sound scientific results for the first few hundred years of CESM-HR simulations on the new architecture. Finally, the summary and discussions are given in Sec.
Each CPU consists of four core groups, and each core group includes a memory controller, a master core i. MPE — management processing element , and 64 slave cores i. The heterogeneous many-core processor SW as shown in the name, a core CPU provides all of the computing capabilities of the system.
Each SW processor, as shown in Fig. The running frequency of each element is 1. Most current scientific computing models are constrained by the memory bandwidth rather than the computing speed.
As a result, a large part of our effort tries to achieve a suitable mapping of the CESM model to the unique memory hierarchy of Sunway TaihuLight. A major difference is that the same level of DDR3 memory bandwidth needs to be shared among a significantly larger number of cores from dozens to hundreds.
The major changes are in the CPEs. Such a change in cache hierarchy requires a complete rethinking of both data and loop structures. Previous programmers could rely on the cache hierarchy to achieve a reasonable buffering of temporary variables when using OpenMP Open Multi-Processing to start independent threads in different CPU cores. Migrating to Sunway, the programmers need to handle the memory part more elegantly to achieve any meaningful utilization of the system.
The scratchpad fast buffer also becomes the last weapon for the programmers to address the proportionally reduced memory bandwidth of the system. In many cases, the manually designed buffering scheme would improve data reuse and increase the computing performance. As a result, instead of directly reading from the DDR memory, most kernels would load the data into LDM manually, and start the computation from there.
Such an integration approach can provide high-performance computing power in a high-density form. The TaihuLight computing nodes are connected via a two-level InfiniBand network. A single switch with full bisection bandwidth connects all nodes within a super-node, while a fat-tree network with one-quarter of the full bisection bandwidth connects all super-nodes, with bisection communication bandwidth at different levels.
Table 1 The list of diagnostic software tools in the Sunway system. Targeting a completely new many-core processor and system with over 10 million cores, the compiler tools are probably the most important tools to support application development of the Sunway TaihuLight.
In addition to that, a parallel compilation tool supports the OpenACC 2. This customized Sunway OpenACC tool supports management of parallel tasks, extraction of heterogeneous code, and description of data transfers.
In addition to Sunway OpenACC, the Sunway platform also provides an Athread interface for the programmers to write specific instructions for both the computing and the memory parts. As discussed in later sections, different approaches are taken for the redesign of different parts of the CESM model.
Earth system models are some of the most complex numerical models that scientists have ever built. Unfortunately, these models are also some of the very first software that were ported to the new Sunway TaihuLight system and tested with the relatively new compiler system.
As a result, to facilitate the development, a number of new compiling and profiling tools that have been developed along the way of porting and redesigning the model are listed in Table 1, which describes the main function of each tool and the role it plays in this project. This will be discussed in more detail in Sect. As shown in Fig. Finding an optimal balance between the computational load on CPEs and the communication of between MPEs and CPEs is the core of CPE parallelism and the major challenge to obtaining computational efficiency of a large model on the Sunway machine, which will be discussed in more detail in Sect.
Figure 2 Illustration of the second-level parallelism of CPE-based task decomposition required on the Sunway heterogeneous many-core machine, which is additional to the first-level parallelism of domain-based task decomposition among core groups and the MPI parallelism as in the homogeneous multicore system. In order to achieve extreme performance and power efficiency, the computing chip of Sunway TaihuLight SW CPU abandons the cache structure so as to spare the on-chip resources for more computing occupancy.
As a result, each CG of the SW is able to deploy 64 slave cores, and the 64 slave cores can communicate with each other based on register communications. Therefore, a more fine-grained communication mechanism is provided to allow for more sophisticated operations and optimizations on dealing with data.
Such a behaviour is different from the conventional cases such as the CPU—GPU scenario where data transfer has to go between different processors and accelerators. On the other hand, the on-chip heterogeneity also leads to the uniform programming model between MPE and CPEs, and it is promising for resolving problems such as the loop code.
In that sense, the Sunway architecture may be more plausible for scientific computation like Earth system modelling than a GPU system. It can be seen that the Sunway TaihuLight, due to the lower chip frequency 1. However, considering that weather and climate applications do not maximize the floating-point operations per second FLOPS provided by the computer, those numbers do not demonstrate that the Sunway TaihuLight system has any real advantage in actual power efficiency.
To conclude on this point, more precise calculations taking into account the real FLOPS of the applications and including the human labour cost of the porting and optimization effort given that Sunway TaihuLight is a unique system have to be realized. Table 2 Power efficiency of some major systems. This latter model tag was developed specifically for supporting a high-resolution CESM version with 0. The details of this progression from CESM1. The new atmospheric physics results in better positioning of the Southern Hemisphere jet and improved high- and low-cloud simulations, with general increases in low cloud, producing better agreement with observations.
The first one is a new iterative solver for the barotropic mode to reduce communication costs, which is especially good for high-resolution simulations on large processor counts e.
Hu et al. There were also a few sea-ice namelist parameter changes as in Small et al. Finally, to improve sea-ice thickness and extent simulation, further adjustments have been done to the melting snow grain radius and the melt onset temperature to increase the sea-ice albedo with the effect of thickening the sea ice and increasing the extent. Note that although these CESM 1. The structure of the CESM-HR model illustrates the challenge of the project: migrating a combination of several complex models onto a completely new architecture.
The atmosphere, the ocean, the land, the sea-ice, and the coupler models can each be fairly complicated on their own with hundreds of thousands of lines of code. Using the analysis tool swlu mentioned in Sect. Figure 4 The major consumers of run time in the CAM5 model. Therefore, in the process of migrating towards a new machine, CAM5 and POP2 are the main targets that require refactoring and redesign, while the other components require only porting effort.
So you have 2 regular Pi Zero and one Wireless Pi connected. This is done using a Proxy over the USB. Using the directions above I was able to cluster the 3 Wireless Pi together and then have the 6 slave Pi work in the back ground as sub clusters of each Wireless Pi.
Instead of grouping all 9 of them together each member of the wireless cluster can draw on resources of the other two regular Pi as needed. I used a throttling script to distribute the work load across the cluster and some load balancing scripts to control the 6 slave Pi.
Think of it as having 3 managers and 6 employees. Each manager has 2 employees helping with a task in the background. Employees can only share resources with other employees via the Manager. Each Manager takes the resources and shares it with its own employees or other managers.
Question 7 months ago on Step 7. Question 2 years ago. Is there anywhere else I can download the mpi4py from? Answer 2 years ago. Reply 8 months ago. Answer 1 year ago. Question 8 months ago on Step 9. Run into an issue, sudo aptitude doesn't work. Run into a wall at step 5. Changed aptitude to apt, was able to proceed. Getting a tar-zxf not found message, tho. Question 11 months ago. Hello, can a cluster like this be used to process videos made in OpenShot Video Editor.
So, can a cluster be used to process and export videos? Question 1 year ago. Once I moved to this model can I go back to single board raspberry PI? Like everytime I start my raspberry pi do I need to have both of them connected? Question 2 years ago on Step 9.
I have CNN model, which segregates images. Can I save some time using this cluster i built of course, with your help. The problem is the technology has moved on and they haven't bothered to update the command line codes. This is riddled with bugs!!! I had to search the internet for hours just to find updated code. Reply 2 years ago. As it notes above, the technology worked at the time of writing.
Can you provide any further information on how you got around the technicalities and provide the updated code to help others create this project on the new version of RPI? Considering the demand for e-reading in cross-platform devices we have created Books with separate editions optimized for devices of different screen size like PCs,Tablets and Mobiles. Membership registration is required to download pdf books online from our library.
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