Case StudySoftware

Figure 1. Illegal image filter system architecture

Optimizing an Illegal Image Filter System

Tencent Doubles the Speed of its Illegal Image Filter System using SIMD Instruction Set and Intel® Integrated Performance Primitives

For a large Internet service provider like China’s Tencent, being able to detect ille-gal images is important. Popular apps like WeChat*, QQ*, and QQ Album* are notjust text oriented, but also image generation and sharing apps. Every year, the vol-ume of newly generated images reach about 100 petabytes―even after imagecompression. Some users may try to upload illegal images (e.g., porn). They cer-tainly don’t tell the system that the image is illegal, so the system runs a check oneach image to try to block them. This is a huge computing workload, with billionsof images uploaded each day.

Technical Background

When a user uploads an image, the filter system decodes the image for prepro-cessing. The preprocessing uses a Filter2D function to smooth and resize animage of any size to one fixed size. This intermediate image is the input for finger-print creation. The new fingerprint is compared to the seed fingerprint of an illegalimage. If the image is illegal, it is blocked or deleted and the fingerprint is addedinto the illegal image fingerprint repository.

Figure 1 shows how a fingerprint seed is added to the repository manually.

Intel® Integrated Performance Primitives

High-Performance Computing

Figure 2. Illegal image filter system hotspot functions

and how the application can benefit fromavailable hardware resources.

Using Intel VTune Amplifier to profileTencent’s illegal image filter system, theteam was able to locate the hotspot func-tions. Figure 2 shows that GetRegionID,GetFingerGrayGegionHistogram andcv::Filter2D are the top three hotspotfunctions.

The team found that GetRegionID wascalled by GenFingerGrayRegionHistogram.The GetRegionID function included a cas-caded clause (if else), which is hard to opti-mize. The GetFingerGrayGegionHistogramfunction can be reimplemented usingSIMD instructions. And the filter2D func-tion can be reimplemented by Intel® Inte-grated Performance Primitives (Intel® IPP).Both these functions achieved more than a10x speed-up. The whole system hasmore than doubled its speed, whichhelped Tencent double the performanceof its system. Moreover, the latency reduc-tion improves the usage experience whenusers share images and send them tofriends. Since Filter2D is also widely usedin data processing and digital image pro-cessing, Intel’s work with Tencent is a goodexample for other cloud service users.

Intel® Streaming SIMD Extensionsand GetFingerGrayGegionHistogramOptimization Intel introduced an instruction set exten-sion with the Intel® Pentium® III processorcalled Intel® Streaming SIMD Extensions(Intel® SSE). This was a major extensionthat added floating-point operationsover the earlier, integer-only SIMD in-struction set called MMX™, which was in-troduced with the Intel Pentiumprocessor. Since the original Intel SSE,SIMD instruction sets have been ex-tended by wider vectors, new and exten-sible syntax, and rich functionality. Thelatest SIMD instruction, set Intel® Ad-vanced Vector Extensions (Intel®AVX512), can be found in the Intel® Core™i7 processor.

Figure 3. Before and after optimization

Optimizing an Illegal  Image Filter System 2

Optimization OverviewWorking with Tencent engineers, Intelprepared the environment to tune thehotspot functions. Intel® VTune™ Ampli-fier is a powerful tool that can provide

runtime information on code perform-ance for developers creating serial andmultithreaded applications. With this pro-filer information, a developer can analyzethe algorithm choices and identify where

Tencent More than Doubles the Speed and Performance of Filtering

developers with ready-to-use, proces-sor-optimized functions to accelerateimage, signal, data processing, and cryp-tography computation tasks. It suppliesrich functions for image processing, in-cluding an image filter, which was highlyoptimized using SIMD instructions.

Optimizing Filters 2D Filtering is a common image processingoperation for edge detection, blurring,noise removal, and feature detection.General linear filters use a general rec-tangular kernel to filter an image. Thekernel is a matrix of signed integers orsingle-precision real values. For eachinput pixel, the kernel is placed on theimage in such a way that the fixed an-chor cell (usually be geometric center)within the kernel coincides with theinput pixel. Then it computes the accu-mulation of the kernel and the corre-sponding pixel. Intel IPP supports thevariant filter operation (Table 2).

For several border types (e.g., constantborder, replicated border), Tencent usedOpenCV* 2.4.10 to preprocess images.Using Intel VTune Amplifier XE, the teamfound that the No. 3 hotspot is cv::Fil-ter2D. It was convenient and easy to usethe Intel IPP filter function to replace theOpenCV function.

The test file filter2D_demo.cpp is fromOpenCV Version: 3.1:

• @brief Sample code that showshow to implement your own linearfilters by using filter2D function

• @author OpenCV team

The team used the IPP filter functionfrom Intel IPP 9.0, update 1, as shown inFigure 4.

Accelerating the Filter2D by Intel IPPThe team used the same filter kernel tofilter the original image, and then recom-piled the test code and OpenCV codewith GCC and benchmarked the result onIntel Xeon processor-based systems. Theeffects of the image filter are the samefrom Intel IPP code and OpenCV code.

Algorithms that significantly benefitfrom SIMD instructions include a widerange of applications such as image andaudio/video processing, data transfor-mation and compression, financial ana-lytics, and 3D modeling and analysis.

The hardware that runs Tencent’s illegalimage filter system uses a mix of proces-sors. Intel’s optimization is based thecommon SSE2, which is supported by allthe Tencent systems.

The team investigated the top two func-tions: GetRegionID and GetFingerGray-GegionHistogram. GetRegionID wascalled by GenFingerGrayRegionHis-togram. The GetRegionID function in-cludes a cascaded clause of “if else,”which is hard to optimize. But the func-tion GetFingerGrayGegionHistogram hasa big “for” loop for each image pixel,which has room for optimization. Andthere is a special algorithm logical whenGetFingerGrayGegionHistogram callsGetRegionID. Intel rewrote part of the

code to use the SIMD instruction to opti-mize the GetFingerGrayGegionHis-togram function. Figure 3 showsSimhash.cpp, which is the source file be-fore optimization, and simhash11.cpp,the file after optimization.

Intel tested the optimized code on anIntel® Xeon® processor E5-2680 0 at2.70GHz. The input image was a largeJPEG image (5,000 by 4,000 pixels). Ten-cent calls the function by single thread,so the team tested it on single core. Theresult was a significant speedup (Table 1).

Intel® IPP and Filters 2D OptimizationIntel SIMD capabilities change over time,with wider vectors, newer and more ex-tensible syntax, and rich functionality. Toreduce the effort needed on instruction-level optimization to support everyprocessor release, Intel offers a store-house of optimized code for all kinds ofspecial computation tasks. The Intel IPPlibrary is one of them. Intel IPP provides

Table 1. Traits and behaviors of mutexes

AlgorithmName

Tencent’soriginal

SSE4.2optimized

Latency (Seconds)(Smaller is Better)

0.457409

0.016346

Gain

100%

2,798%

Table 2. Variant filter operation

IPP Filter Functionality

ppiFilter<Bilateral|Box|SumWin-dow>Border

ippiMedian Filter

ippiGeneral Linear Filters

Separable Filters

Wiener Filters

Fixed Filters

Convolution

Notes

Perform bilateral, box filter, sum pixel inwindow

Median filter

A user-specified filter

FilterColumn or FilterRow function

Wiener filter

Gaussian, laplace, hipass and lowpass fil-ter, Prewitt, Roberts and Scharr filter etc.

Image convolution

Optimizing an Illegal  Image Filter System 3

Optimizing an Illegal  Image Filter System4

Figure 5 shows the Intel IPP smoothimage (right) compared to originalimage.

The consumption times (Table 3) showthat the ipp_filter2D take less time(about 9ms), while the OpenCV sourcecode takes about 143ms. The IPP fil-ter2D is 15x faster than the OpenCVplain code.

Note that OpenCV has Intel IPP inte-grated during compilation; thus, somefunctions can call Intel IPP functions un-derlying automatically. In some ofOpenCV versions, the Filter2d functioncan at least call the IPP Filter2D. But,considering the overhead of functioncalls, the team selected to call the IPPfunction directly.

SummaryWith manual modifications to the algo-rithm, plus SSE instruction optimizationand Intel IPP filter replacement, Tencentreports that the performance of its ille-gal image filter system has more thandoubled compared to the previous im-plementation in its test machine. Figure6 shows the total results (category 3).

Tencent is a leading Internet serviceprovider with high performance require-ments for all kinds of computing tasks.Detecting and filtering illegal images is atypical example. Popular apps likeWechat, QQ, and QQ Album generatebillions of images each day.

Intel helped Tencent optimize the topthree hotspot functions of its illegalimage filter system. By manually imple-menting the fingerprint generation withSIMD instructions, and replacing the fil-ter2D function with a call into the IntelIPP library, Tencent was able to speedup both hotspots by more than 10x. As aresult, the entire illegal image filter sys-tem has more than doubled its speed.This will help Tencent double the capa-bility of its systems. Moreover, the la-tency reduction improves the userexperience for customers sharing andsending images.

Figure 4. Code before (left) and after (right) using IPP

Figure 5. Image before (left) and after filtering

Table3. Consumption times

CPU

Intel® Xeon®processor E5-

2680 v3

Program

ipp_filter2D

OpenCV_filter2D

Time (ms)

9

143

Optimizing an Illegal  Image Filter System 7

Figure 6. Total results

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