OpenCL Performance Evaluation on Modern Multi Core CPUs (original) (raw)

Performance Evaluation and Improvements of the PoCL Open-Source OpenCL Implementation on Intel CPUs

International Workshop on OpenCL, 2021

The Portable Computing Language (PoCL) is a vendor independent open-source OpenCL implementation that aims to support a variety of compute devices in a single platform. Evaluating PoCL versus the Intel OpenCL implementation reveals significant performance drawbacks of PoCL on Intel CPUs-which run 92 % of the TOP500 list. Using a selection of benchmarks, we identify and analyse performance issues in PoCL with a focus on scheduling and vectorisation. We propose a new CPU device-driver based on Intel Threading Building Blocks (TBB), and evaluate LLVM with respect to automatic compiler vectorisation across work-items in PoCL. Using the TBB driver, it is possible to narrow the gap to Intel OpenCL and even outperform it by a factor of up to 1.3× in our proxy application benchmark with a manual vectorisation strategy. CCS CONCEPTS • General and reference → General conference proceedings; • Software and its engineering → Parallel programming languages; Multithreading; Scheduling; Compilers.

An investigation of the performance portability of OpenCL

This paper reports on the development of an MPI/OpenCL implementation of LU, an application-level benchmark from the NAS Parallel Benchmark Suite. An account of the design decisions addressed during the development of this code is presented, demonstrating the importance of memory arrangement and work-item/work-group distribution strategies when applications are deployed on different device types. The resulting platform-agnostic, single source application is benchmarked on a number of different architectures, and is shown to be 1.3-1.5x slower than native FORTRAN or CUDA implementations on a single node and 1.3-3.1x slower on multiple nodes. We also explore the potential performance gains of OpenCL's device fissioning capability, demonstrating up to a 3x speed-up over our original OpenCL implementation.

Improving Performance Portability in OpenCL Programs

We study the performance portability of OpenCL across diverse architectures including NVIDIA GPU, Intel Ivy Bridge CPU, and AMD Fusion APU. We present detailed performance analysis at assembly level on three exemplar OpenCL benchmarks: SGEMM, SpMV, and FFT. We also identify a number of tuning knobs that are critical to performance portability, including threads-data mapping, data layout, tiling size, data caching, and operation-specific factors. We further demonstrate that proper tuning could improve the OpenCL portable performance from the current 15% to a potential 67% of the state-of-the-art performance on the Ivy Bridge CPU. Finally, we evaluate the current OpenCL programming model, and propose a list of extensions that improve performance portability.

Performance Traps in OpenCL for CPUs

2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2013

With its design concept of cross-platform portability, OpenCL can be used not only on GPUs (for which it is quite popular), but also on CPUs. Whether porting GPU programs to CPUs, or simply writing new code for CPUs, using OpenCL brings up the performance issue, usually raised in one of two forms: "OpenCL is not performance portable!" or "Why using OpenCL for CPUs after all?!". We argue that both issues can be addressed by a thorough study of the factors that impact the performance of OpenCL on CPUs. This analysis is the focus of this paper. Specifically, starting from the two main architectural mismatches between many-core CPUs and the OpenCL platform-parallelism granularity and the memory model-we identify eight such performance "traps" that lead to performance degradation in OpenCL for CPUs. Using multiple code examples, from both synthetic and real-life benchmarks, we quantify the impact of these traps, showing how avoiding them can give up to 10 times better performance. Furthermore, we point out that the solutions we provide for avoiding these traps are simple and generic code transformations, which can be easily adopted by either programmers or automated tools. Therefore, we conclude that a certain degree of OpenCL interplatform performance portability, while indeed not a given, can be achieved by simple and generic code transformations.

Improving CPU Performance For Heterogeneous Computing Using OpenCL

International journal of engineering research and technology, 2013

With the rapid development of smart phones, tablets, and pads, there has been widespread adoption of Graphic Processing Units (GPUs). The hand-held market is now seeing an ever-increasing rate of development of computationally intensive applications, which require significant amounts of processing resources. To meet this challenge, GPUs can be used for general-purpose processing. We are moving towards a future where devices will be more connected and integrated. This will allow applications to run on handheld devices, while offloading computationally intensive tasks to other compute units available. There is a growing need for a general programming framework which can utilize heterogeneous processing units such as GPUs and DSPs. OpenCL, a widely used programming framework has been a step towards integrating these different processing units on desktop platforms. A thorough study of the factors that impact the performance of OpenCL on CPUs. Specifically, starting from the two main arc...

OpenCL Performance Evaluation on Multiple Operating Systems

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023

OpenCL is an open standard for parallel computing that enables performance portability across diverse computing platforms. In this work, we perform a systematic evaluation of OpenCL performance on several Operating System Platforms including Windows, Linux, Android and macOS. Our results provide insights into the impact of the Operating Systems on OpenCL performance and identify any potential performance bottlenecks. We also compare performance of OpenCL with other parallel computing frameworks like Nvidia’s CUDA (Compute Unified Device Architecture), Apple’s Metal framework, DirectX Compute etc. on different operating systems to better understand the trade-offs between different OSs. Our findings can help researchers and practitioners make informed decision about choosing the appropriate Operating System for their OpenCL applications and guide future development of OpenCL standard.

pocl: A Performance-Portable OpenCL Implementation

International Journal of Parallel Programming, 2014

OpenCL is a standard for parallel programming of heterogeneous systems. The benefits of a common programming standard are clear; multiple vendors can provide support for application descriptions written according to the standard, thus reducing the program porting effort. While the standard brings the obvious benefits of platform portability, the performance portability aspects are largely left to the programmer. The situation is made worse due to multiple proprietary vendor implementations with different characteristics, and, thus, required optimization strategies. In this paper, we propose an OpenCL implementation that is both portable and performance portable. At its core is a kernel compiler that can be used to exploit the data parallelism of OpenCL programs on multiple platforms with different parallel hardware styles. The kernel compiler is modularized to perform target-independent parallel region formation separately from the target-specific parallel mapping of the regions to enable support for various styles of fine-grained parallel resources such as subword SIMD extensions, SIMD datapaths and static multi-issue. Unlike previous similar techniques that work on the source level, the parallel region formation retains the information

Design of OpenCL Framework for Embedded Multi-core Processors

In modern mobile embedded systems, various energy-efficient hardware acceleration units are employed in addition to a multi-core CPU. To fully utilize the computational power in such heterogeneous systems, Open Computing Language (OpenCL) has been proposed. A key benefit of OpenCL is that it works on various computing platforms. However, most vendors offer OpenCL software development kits (SDKs) that support their own computing platforms. The study of the OpenCL framework for embedded multi-core CPUs is in a rudimentary stage. In this paper, an OpenCL framework for embedded multi-core CPUs that dynamically redistributes the time-varying workload to CPU cores in real time is proposed. A compilation environment for both host programs and OpenCL kernel programs was developed and OpenCL libraries were implemented. A performance evaluation was carried out with respect to various definitions of the device architecture and the execution model. When running on embedded multi-core CPUs, applications parallelized by OpenCL C showed much better performance than the applications written in C without parallelization. Furthermore, since programmers are capable of managing hardware resources and threads using OpenCL application programming interfaces (APIs) automatically, highly efficient computing both in terms of the performance and energy consumption on a heterogeneous computing platform can be easily achieved 1 .

The Feasibility of Using OpenCL Instead of OpenMP for Parallel CPU Programming

OpenCL, along with CUDA, is one of the main tools used to program GPGPUs. However, it allows running the same code on multi-core CPUs too, making it a rival for the long-established OpenMP. In this paper we compare OpenCL and OpenMP when developing and running compute-heavy code on a CPU. Both ease of programming and performance aspects are considered. Since, unlike a GPU, no memory copy operation is involved, our comparisons measure the code generation quality, as well as thread management efficiency of OpenCL and OpenMP. We evaluate the performance of these development tools under two conditions: a large number of short-running compute-heavy parallel code executions, when more thread management is performed, and a small number of long-running parallel code executions, when less thread management is required. The results show that OpenCL and OpenMP each win in one of the two conditions. We argue that while using OpenMP requires less setup, OpenCL can be a viable substitute for Open...

An OpenCL framework for heterogeneous multicores with local memory

2010

In this paper, we present the design and implementation of an Open Computing Language (OpenCL) framework that targets heterogeneous accelerator multicore architectures with local memory. The architecture consists of a general-purpose processor core and multiple accelerator cores that typically do not have any cache. Each accelerator core, instead, has a small internal local memory. Our OpenCL runtime is based on software-managed caches and coherence protocols that guarantee OpenCL memory consistency to overcome the limited size of the local memory. To boost performance, the runtime relies on three source-code transformation techniques, work-item coalescing, web-based variable expansion and preload-poststore buffering, performed by our OpenCL C source-to-source translator. Work-item coalescing is a procedure to serialize multiple SPMD-like tasks that execute concurrently in the presence of barriers and to sequentially run them on a single accelerator core. It requires the webbased variable expansion technique to allocate local memory for private variables. Preload-poststore buffering is a buffering technique that eliminates the overhead of software cache accesses. Together with work-item coalescing, it has a synergistic effect on boosting performance. We show the effectiveness of our OpenCL framework, evaluating its performance with a system that consists of two Cell BE processors. The experimental result shows that our approach is promising.