Thanks to Moore's law, servers have become faster and more affordable for decades. However, recent demand for computing power has outpaced these advancements, causing organizations to turn to distributed computing and communication increasingly. Below we explore the fundamental concepts of distributed computing and why it is crucial in today's technology landscape.
The Power of Distributed Computing
Distributed computing uses the collective power of multiple cores, processors, and computers to solve complex problems. Early supercomputers were massive individual machines, but the concept of connecting ordinary servers through high-speed networks gave rise to distributed computing. Over time, standards like the Message Passing Interface (MPI) and Partitioned Global Address Space (PGAS) programming were developed to coordinate communication and synchronization among distributed systems. Both programming models have their own benefits and applications:
Message Passaging Interfaces (MPIs) allows message passing between program instances. At a high level, it's like running the same software in as many as 1000 instances and having them work together to solve a problem. And unlike a group of 1000 humans, for example, these applications can work together in such a way that gets work done faster.
Alternatively, PGAS provides a shared memory model across multiple computers. Therefore, programmers can build their applications to leverage separate computers like they would numerous processors within a single computer.
Some computing tasks can be parallelized (duplicated and performed simultaneously) within a single processor. One example is vectorization, supported by modern computer architectures like Intel Architecture, ARM, PowerPC, and RISC-V. By leveraging vectorization, compilers can optimize code to perform multiple operations in a single instruction making for faster overall processing. Further, innovators looking to leverage parallel processing must take several considerations into account, including:
Today's processors often come with multiple cores, which provide parallel processing capabilities. These cores operate on a shared memory system known as coherent shared memory. Building on multi-core systems can be challenging, but various abstractions, such as processes and threads, help manage parallelism at different levels. Processes represent independent programs, while threads are units of execution within a single process. One caveat is that programming at the thread level requires careful consideration to avoid synchronization issues.
Programming with Parallel Processing in Mind
Parallel languages, like Erlang, Julia, and CILK, allow developers to apply parallelism to their entire programs. Alternatively, language extensions like OpenMP can be used with familiar languages like C and C++. For example, OpenMP enables parallelizing specific code sections by adding simple directives. Additionally, specialized parallel languages like SYCL, CUDA, and OpenCL are used for programming hardware accelerators like GPUs and FPGAs.
Developing thread-level parallelism can be complex. Parallel libraries, such as Intel oneAPI Threading Building Blocks (oneTBB), offer prebuilt and tested functions that implement common parallel design patterns to simplify the process. In addition, these libraries provide higher levels of abstraction, enabling developers to focus on the overall logic of their parallel programs without worrying about low-level details.
Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs) have gained prominence as hardware accelerators. Initially designed for graphics, GPUs are now widely used for general-purpose parallel computing. NVIDIA CUDA and the open-source OpenCL framework provide programming interfaces for GPU offloading. Similarly, FPGAs offer high-performance computing by allowing the modification of circuitry to suit specific applications. In addition, the emergence of programming systems like SYCL and the Intel oneAPI DPC++ Compiler simplifies code development that can run on both CPUs and GPUs.
As exciting as distributed computing and parallelism are, they are not entirely new concepts. They are at work in much of the technology we use today and make possible things that are easy to take for granted, like:
- Ten-day weather forecasts that are as accurate as possible
- Quick and easy access to the Internet
- Improved design processes that ensure that planes, spacecraft, bridges, and nuclear reactors will work immediately after being built
- Optimized wind turbine blades, race yacht sails, and keels
- Better-than-ever drilling discovery for oil, water, or rare minerals
This is not to say that single processors can't do the needed work. Instead, they would take much longer to perform the same operations, making them impractical.
When Designing Your Distributed Computing Environment, Harness the Power of Intel and UNICOM Engineering
Distributed computing and distributed communication are vital in addressing the ever-increasing demand for computing power. However, as a result, growing organizations must determine how to find and integrate the proper hardware to support their distributed computing needs.
As an Intel Technology Provider and Dell Technologies Titanium OEM partner, UNICOM Engineering stands ready to design, build, and deploy the right hardware solution for your next Distributed Computing, AI, Deep Learning, or HPC initiative. Our deep technical expertise can drive your transitions to next-gen platforms and provide the flexibility and agility required to bring your solutions to market.
Leading technology providers trust UNICOM Engineering as their application deployment and systems integration partner. And our global footprint allows your solutions to be built and supported worldwide by a single company. Schedule a consultation today to learn how UNICOM Engineering can assist your business.