Intel OneAPI Outperforms in Complex Computational Problems, Study Reveals

Intel OneAPI, a tool developed by Intel, has been assessed for its performance in solving complex computational problems, specifically heat diffusion and image denoising. The study found that the CPU-iGPU scheme outperformed the fastest device when the problem was computationally demanding. However, the CPU-FPGA scheme was affected by bandwidth limitations. The study concluded that Intel OneAPI is a useful tool for multi-platform development, but specific code for each platform is necessary for optimal performance. The future of heterogeneous computing looks promising with tools like Intel OneAPI, but efficient memory management and dynamic workload balancing will be crucial.

What is the Intel OneAPI and its Capabilities?

Intel OneAPI is a tool developed by Intel to solve complex computational problems. In a recent study conducted by Silvia R Alcaraz, Ruben Laso, Oscar G Lorenzo, David L Vilariño, Tomás F Pena, and Francisco F Rivera, the performance of a heterogeneous application developed with Intel OneAPI was assessed. The application was designed to solve two well-known diffusion problems: heat diffusion and image denoising. The researchers explored CPU-iGPU and CPU-FPGA schemes, applying dynamic load balancing and conducting experiments on Intel DevCloud.

The results of the study demonstrated that the CPU-iGPU scheme outperformed the execution times achieved by the fastest device when the problem was sufficiently computationally demanding. However, the performance of the CPU-FPGA scheme was heavily affected by bandwidth limitations, requiring specific strategies to manage memory efficiently. The study also found that dynamic workload balancing is crucial due to possible performance fluctuations in any of the implicated devices.

In conclusion, Intel OneAPI provides a helpful tool for multi-platform development using a unique high-level language, DPC. However, developing specific code for each platform is necessary to achieve optimal performance.

What is Heterogeneous Computing?

Heterogeneous computing refers to the use of multiple processing units or accelerators within a unified computing system, which may have different architectures or capabilities. It aims to leverage the strengths inherent in each of these devices to attain a range of advantages, including enhanced performance and reduced energy consumption among others.

Personal computers or laptops are usually equipped with a CPU and an integrated and/or dedicated GPU. This is related to the GPGPU (General-Purpose Computing on Graphics Processing Units) concept, which is based on leveraging the computing power of GPUs for general-purpose applications, not just for rendering graphics. The most popular platforms to develop GPGPU applications are CUDA (Compute Unified Device Architecture) to work with NVIDIA GPUs and OpenCL (Open Computing Language), an open framework that can run on various devices including CPUs, GPUs, and other accelerators.

What are the Advantages of FPGAs?

Field Programmable Gate Arrays (FPGAs) are devices that can be included to create heterogeneous schemes. In the past, the use of FPGAs increased due to their capacity to adapt their architecture to a specific problem based on programmable logic. Another advantage is their low power consumption, as demonstrated in several studies.

Despite the advantages mentioned about FPGAs, their popularity did not reach the magnitude of GPUs, mainly because of the maturity in the software stack provided by the latter. The standard way to program FPGAs requires using hardware description languages such as VHDL or Verilog and low-level knowledge of hardware and circuitry to develop optimal solutions. However, FPGAs are currently experiencing a new resurgence thanks to the new high-level development tools.

How Does Performance Portability Work?

Performance portability refers to the capacity of a code to be executed efficiently across different platforms without the necessity for significant manual modifications. Strategies for parallelising a problem or optimising its performance are different across devices. To develop efficient codes for GPUs, it is essential to understand the thread execution model to maximise the utilisation of their computing capabilities. Additionally, achieving coalescent accesses and allocating data efficiently is equally important to best use the memory. For dGPUs (dedicated GPUs), careful consideration is necessary when deciding which data to send to on-chip memory to avoid costly memory communications.

What is the Future of Heterogeneous Computing?

The future of heterogeneous computing looks promising with the advent of tools like Intel OneAPI. As applications or programs demand more computing power, manufacturers have also been producing more capable devices. This evolution can be traced to the rise in powerful processors and larger memory capacities. Moreover, there has been a growth of devices harnessing the advantages offered by heterogeneous computing.

However, achieving optimal performance in heterogeneous computing requires developing specific code for each platform. This is where tools like Intel OneAPI come into play, providing a high-level language for multi-platform development. As the field continues to evolve, the need for efficient memory management and dynamic workload balancing will become increasingly important.