Optical scheme for hardware/software partitioning with semaphore

Improve security. In some cases, you can separate sensitive and nonsensitive data into different partitions and apply different security controls to the sensitive data.

Provide operational flexibility. Partitioning offers many opportunities for fine-tuning operations, maximizing administrative efficiency, and minimizing cost.

For example, you can define different strategies for management, monitoring, backup and restore, and other administrative tasks based on the importance of the data in each partition. Match the data store to the pattern of use. Partitioning allows each partition to be deployed on a different type of data store, based on cost and the built-in features that data store offers.

For example, large binary data can be stored in blob storage, while more structured data can be held in a document database. See Choose the right data store. Improve availability.

Separating data across multiple servers avoids a single point of failure. If one instance fails, only the data in that partition is unavailable.

Operations on other partitions can continue. For managed PaaS data stores, this consideration is less relevant, because these services are designed with built-in redundancy.

Horizontal partitioning often called sharding. In this strategy, each partition is a separate data store, but all partitions have the same schema. Each partition is known as a shard and holds a specific subset of the data, such as all the orders for a specific set of customers.

Vertical partitioning. In this strategy, each partition holds a subset of the fields for items in the data store. The fields are divided according to their pattern of use. For example, frequently accessed fields might be placed in one vertical partition and less frequently accessed fields in another. Functional partitioning.

In this strategy, data is aggregated according to how it is used by each bounded context in the system. For example, an e-commerce system might store invoice data in one partition and product inventory data in another. These strategies can be combined, and we recommend that you consider them all when you design a partitioning scheme. For example, you might divide data into shards and then use vertical partitioning to further subdivide the data in each shard.

Figure 1 shows horizontal partitioning or sharding. In this example, product inventory data is divided into shards based on the product key. Each shard holds the data for a contiguous range of shard keys A-G and H-Z , organized alphabetically. Sharding spreads the load over more computers, which reduces contention and improves performance. The most important factor is the choice of a sharding key.

It can be difficult to change the key after the system is in operation. The key must ensure that data is partitioned to spread the workload as evenly as possible across the shards.

The shards don't have to be the same size. It's more important to balance the number of requests. Some shards might be very large, but each item has a low number of access operations. Other shards might be smaller, but each item is accessed much more frequently.

It's also important to ensure that a single shard does not exceed the scale limits in terms of capacity and processing resources of the data store. Avoid creating "hot" partitions that can affect performance and availability. For example, using the first letter of a customer's name causes an unbalanced distribution, because some letters are more common.

Instead, use a hash of a customer identifier to distribute data more evenly across partitions. Choose a sharding key that minimizes any future requirements to split large shards, coalesce small shards into larger partitions, or change the schema. These operations can be very time consuming, and might require taking one or more shards offline while they are performed. If shards are replicated, it might be possible to keep some of the replicas online while others are split, merged, or reconfigured.

However, the system might need to limit the operations that can be performed during the reconfiguration. For example, the data in the replicas might be marked as read-only to prevent data inconsistences.

For more information about horizontal partitioning, see sharding pattern. Figure 2 shows an example of vertical partitioning. In this example, different properties of an item are stored in different partitions.

One partition holds data that is accessed more frequently, including product name, description, and price. Software starts at the top of the routine and works its way downward.

With hardware you are designing in pieces that run in parallel with each other. Here we see we have two different hardware blocks. The goal here is we are going to start with an HDL file and result in a programming image that we can load into our programmable logic device. And the first step is that we take the HDL, we run it through a process called synthesis, which takes our human readable HDL program and turns it in to a netlist. The netlist is a file that contains logic gates, connections and hard IP.

After that the netlist is then mapped into the target device which are mapped into look up tables, embedded memory and other various pieces that are specific to the target device. And then the connections between the components are then routed. A static timing analysis is performed.

The placements are changed to meet timing. So this is it, but the static timing step is an iterative process. If we don't meet timing, we go back and we reroute, replace in order to improve timing. Once that step is complete, then we create a programming image which is then loaded in the flash.

The other thing I mention here is that the HDL is simulated prior to the synthesis step. This is sort of akin to unit testing in software so we test the HDL; verify that it is functional before that we do that. Simulation step takes a dedicated piece of software simulation to it.

And then you can see the image there. One of the big advantages software has is that software is ubiquitous. There is a wealth of software available in the forms of operating system, freeware etc. Definitely software engineers are more available than hardware engineers.

Another big advantage that processors have over programmable logic is that processors are cheap, comparatively speaking with programmable logic device. And software has a couple of drawbacks. There is limited performance compared with HDL and limited flexibility. Software is limited by the hardware that it runs on. So how do we decide which one to use for a particular application?

This presentation intends to address that very question. We will examine several case studies implemented in both FPGA logic and software running on an embedded microprocessor. We use several metrics to determine the results. We use performance, development schedule, cost, maintainability and power.

We use Xilinx in this case, but the presentation is intended to be vendor agnostic so we could easily use another FPGA vendor such as Altera.

The MicroBlaze is a soft processor which is it means that it is a processor that is actually compiled into the FPGA logic. This is certainly not an overly powerful processor, but it does well for the purposes of comparing hardware versus software. It also provides an easy integration of hardware and software components. We may be able to multiply the performance characteristics of a hard processor by a certain factor. It does the — because it is a soft core in the FPGA fabric, we do have a interface that can be used for hardware offload compiled into the FPGA device.

And just to mention the alternatives since we are being vendor agnostic, the Altera alternatives for your MicroBlaze Processor is the Nios II product. So we are going to examine two case studies for this webinar. The first one is a State Machine. We will be controlling a stepper motor with a state machine and very specific well-defined timing for this particular application. And the second one is going to be a Network Stack. It would be a UDP network stack running on top of internet protocol, and the timing is not strictly defined for this one.

So the network stack certainly has performance requirements, however the real-time hard deadlines are less strict. A network implementation cannot have hard deadlines because networking is by its very nature not strictly defined.

Our first case study is the motor controller. The design considerations that we have are responsiveness to our feedback circuit, the cost of our implementation, reliability, and maintainability. Here we see the details for the hardware implementation for our PWM controller.

On the left-hand side we see the transition from the low to high state. So for our counter, which is constantly running, it is equal to our clock cycle period on the way transition from low to high. Then to transition from back from high to low, we take the feedback from our ADC controller. The details for this hardware implementation, it takes around Flip Flops for this implementation, around Look Up Tables inside the FPGA, which accounts for approximately 0.

In terms of performance, we can run this thing up to around Megahertz and the feedback loop therefore updates within 4 nanoseconds. And this particular implementations takes around 2 mW of power. Here we see the software implementation for our same function. Once it is equal to that percentage then we set our PWM circuit low, and we wait for the signal to be equal to the period of our Kilohertz clock. For the software implementation details for this particular function, this particular routine took instructions.

Because we were running the MicroBlaze at nanoseconds, the response time is therefore 1. The power consumed by the MicroBlaze for this implementation is 24 milliwatt.

Now it is necessary to point out that this is the power that the entire MicroBlaze Processor consumes. If the MicroBlaze was doing other functionality then the power consumption would not increase. For examining the performance of the two different implementations, our hardware design can update based on the feedback within 4 nanoseconds because the design can run at Megahertz.

The software takes a bit longer. For examining the development time or the schedule that it takes to implement these two different options: the hardware took me around 4.

Implementation was longer because of the build time that the FPGA tools took. The software development time was around 2 days, around 8 hours of coding, and then 8 hours of unit test, which included my compile time. I want to point out here that the hardware development time of 4 hours for coding is only less in the software development time because hardware development is a bit fresher on my mind. So it took roughly half the time.

If software were more fresh in my mind then I think the software and hardware coding times would actually be equivalent. There is nothing inherent about hardware coding that is less time consuming than software development. NRE cost is directly proportional to development time. So it follows then that our hardware, because it took longer to implement, is a more expensive NRE cost than is the software.

In our example it took the hardware 4. We can take software and port it to inexpensive microprocessor, that is a lesser expensive microprocessor. So what have we learned from this particular case study? Hardware is more expensive but it is better performing, and in this particular case, actually results in a lower power design. So I think the conclusion that we draw up on this case study is that, unless the hardware solution is absolutely required for our performance reason, we would take the software implementation in this particular design.

And a UDP Ethernet packet will be sent to the data acquisition logic and it will contain parameters that are used for that acquisition things like sample rate, sample size etc. Topology describes the arrangement of the cores here. The mesh partition topology is useful when the total area needs to be reduced. Whereas the ring partition topology reduces the average latency. The heterogeneous and hybrid clustered topology is to reduce the average latency, response time, and keeping the area of the topology constant.

There will always be a trade-off between the partition topology used and the desired performance metrics. According to the application at hand the designer needs to finalize one of them. With a single core supporting number of partitions the architecture is said to be integrated. The reduction in the number of nodes and networks as a consequence of the integration leads to an increase in hardware reliability and a decrease of the hardware cost.

An integrated distributed architecture for mixed-Criticality applications must be decided on a core design that supports the safety requirements of the highest considered criticality class. In VisualSim Architect, the hardware software partitioning can be implemented efficiently, with the model blocks diagram as shown here-.

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