Performance Gains for Ayasdi Analytics* on the Intel® Xeon® Processor E7-8890 V3

Introduction

Ayasdi deploys vertical applications that utilize Topological Data Analysis to extract value from large and complex data. The Ayasdi platform incorporates statistical, geometric, and machine-learning methods through a topological framework to more precisely segment populations, detect anomalies, and extract features.

This paper describes how Ayasdi’s Analytics* running on systems equipped with the Intel® Xeon® processor E7-8890 v3 gained a performance improvement over running on systems with the previous generation of Intel® Xeon® processor E7-4890 v2.

Ayasdi’s Analytic and Intel® Xeon® Processor E7-8890 V3

	Number of Cores	Number of Threads	Memory	Vectorization
Intel® Xeon® processor E7-4890 v2	15	30	DDR3	Intel® Advanced Vector Extensions
Intel® Xeon® processor E7-8890 v3	18	36	DDR4	Intel® Advanced Vector Extensions 2

Table 1. Comparison between processors

Table 1 shows the comparison between the Intel Xeon processor E78890 v3 and Intel Xeon E7-4890 v2. The Intel Xeon processor E78890 v3 has 18 cores compared to the 15 cores of Intel Xeon processor E7-4890 v2, the latter allowing more parallelism resulting in performance improvement. Furthermore, the Intel Xeon processor E7-8890 v3 has a larger memory bandwidth comparing to that of the Intel Xeon processor E7-4890 v2 and uses DDR4 memory while the Intel Xeon processor E7-4890 v2 uses DDR3 memory, thus speeding up the executions.

In terms of software advantages, the Intel Xeon processor E7-8890 v3 supports Intel® Advanced Vector Extensions 2 (Intel® AVX2) while the Intel Xeon processor E7-4890 v2 supports only Intel® Advanced Vector Extensions (Intel AVX). In addition, the Intel Xeon processor E7-8890 v3 introduces Bit Manipulation Instruction sets, BMI1 and BMI2. These instruction sets speed up vector and matrix operations and the core computations of complex machine-learning algorithms.

Ayasdi Analytics was optimized for the Intel Xeon processor E7-8890 v3 by using the new Intel AVX2 intrinsic functions, especially Fused Multiply Add and BMIs. This optimization was accomplished by hand-coding in C++ and through the use of the Intel® Math Kernel Library (Intel® MKL)—Intel MKL version 11.2.

Performance Test Procedure

To show that Intel AVX2 along with the new microarchitecture in the Intel Xeon processor E7 v3 Family increase the throughput of Ayasdi Analytics, we performed tests on two platforms. One system was equipped with the Intel Xeon processor E7-8890 v3 and the other with the Intel Xeon processor E7-4890 v2.

Performance is measured in terms of the following:

The throughput of analyses (analyses per hour) that can be supported by the cluster, with acceptable latency.
The job latency of the analyses in minutes. The job latencies were measured with nine users concurrently accessing the systems.

Test Configurations

System equipped with the Intel Xeon processor E7-8890 v3

System: Pre-production
Processor: Intel Xeon processor E7-8890 v3 @2.5 GHz
Cores: 18
Memory: 1 TB DDR4-1600 MHz

System equipped with the Intel Xeon processor E7-4890 v2

System: Pre-production
Processor: Intel Xeon processor E5-4890 v2 @2.8 GHz
Cores: 15
Memory: 1 TB DDR3-1333 MHz

Operating system: Red Hat Enterprise Linux* 7.0

Application: Ayasdi Analytics Benchmark

Test Results

Figure 1. Performance comparison between processors.

Figure 1 shows a 1.85x performance gain of the system with the Intel Xeon processor E7-8890 v3 over that of the system with the Intel Xeon processor E7-4890 v2. The performance gain is due to the enhanced microarchitecture, increase in core count, better memory type (DDR4 over DDR3), and Intel AVX2.

Figure 2. Latency comparison between processors.

Figure 2 shows the reduction in latency on the system with the Intel Xeon processor E7-8890 v3 over that of the system with the Intel Xeon processor E7-4890 v2. The decrease in latency was credited by the enhanced microarchitecture, core count increase, better memory type (DDR4 over DDR3), and Intel AVX2.

Conclusion

More cores, enhanced microarchitecture, and the support of DDR4 memory contributed to the performance improvement of Ayasdi Analytics on systems equipped with the Intel Xeon processor E7-8890 v3 compared to those with the Intel Xeon processor E7-4890 v2. With the introduction of Intel AVX2, matrix manipulations get a performance boost. In addition, applications that make use of Intel MKL will receive a performance improvement without having to change the source code, since their functions are optimized using Intel AVX2.