For system and architecture researches,  i. e., architecture, OS, networking and storage,  the number of benchmarks will be multiplied by different implementations, and hence become massive.  For example, BigDataBench 3.0 provides about 77 workloads (with different implementations). Given the fact that it is expensive to run all the benchmarks, especially for architectural researches that usually evaluate new design using simulators, downsizing the full range of the BigDataBench benchmark suite to a subset of necessary (non-substitutable) workloads is essential to guarantee cost-effective benchmarking and simulations.


1)    Identify a comprehensive set of workload characteristics from a specific perspective, which affect the performance of workloads.

2)    Eliminate the correlation data in those metrics and map the high dimension metrics to a low dimension.

3)    Use the clustering method to classify the original  workloads into several categories and choose representative workloads from each category.

The methodology details of subsetting (downsizing) workloads are summarized in our IISWC 2014 paper [PDF].

Architecture subset

The BigDataBench architecture subset is for the architecture communities.  Currently, it downsizes the full BigDataBench 3.0— 77 workloads—to 17 representative workloads. Each workload represents a workload cluster with a different size. Note that BigDataBench architecture subset is all from a computer architecture point of view. Results may differ if subsetting is performed from a different point of view.

1.Microarchitectural Metric Selection

We choose a broad set of metrics of different types that cover all major characteristics. We particularly focus on factors that may affect data movement or calculation. For example, a cache miss may delay data movement, and a branch misprediction flushes the pipeline. We choose the 45 metrics from micro-architecture aspects as follows.

  • Instruction Mix
  • Cache Behavior
  • Translation Look-aside Buffer (TLB) Behavior
  • Branch Execution
  • Pipeline Behavior
  • Offcore Requests and Snoop Responses
  • Parallelism
  • Operation Intensity

2. Removing Correlated Data

Given a large number of workloads and metrics, it is difficult to analyze all the metrics to draw meaningful conclusions. Note, however, that some metrics may be correlated. For instance, a long latency cache miss may cause pipeline stalls. Correlated data can skew similarity analysis— many correlated metrics will overemphasize a particular property’s importance. So we eliminate correlated data before analysis. Principle Component Analysis (PCA) is a common method for removing such correlated data. We first normalize metric values to a Gaussian distribution. Then we use Kaiser’s Criterion to choose the number of principle components (PCs). Finally we choose nine PCs, which retain 89.3% variance.

3. Workloads Similarity

In order to show the similarity among each workload, we also employ hierarchical clustering, which is one common way to perform such analysis, for it can quantitatively show the similarity among workloads via a dendrogram. Figure I show the dendrogram, which quantitatively measures the similarity of the full BigDataBench workloads (version 3.0). The dendrogram illustrates how each cluster is composed by drawing a U-shaped link between a non-singleton cluster and its children. The length of the top of the U-link is the distance between its children. The shorter the distance, the more similar between the children. We use Euclidean distance. Further, we use the single linkage distance to create the dendrogram.

Figure 1 Similarity of the full BigDataBench 3.0 workloads.


We use K-Means clustering on the nine principle components obtained from the PCA algorithm to group workloads into similarly behaving application clusters and then we choose a representative workload from each cluster. In order to cluster all the workloads into reasonable classes, we use the Bayesian Information Criterion (BIC) to choose the proper K value. The BIC is a measure of the “goodness of fit” of a clustering for a data set. The larger the BIC scores, the higher the probability that the clustering is a good fit to the data. Here we determine the K value that yields the highest BIC score.

We ultimately cluster the 77 workloads into 17 groups, which are listed in Table I.

Table I Clustering results


1 Cloud-OLTP-Read, Impala-JoinQuery, Shark-Difference, Hadoop-Sort, Cloud-OLTP-San, Ipala-TPC-DS-query8, Impala-Crossproduct, Impala-Project, Impala-AggregationQuery, Cloud-OLTP-Write
2 Hive-TPC-DS-query10, Hive-TPC-DS-query12-1, Hive-Difference, Hadoop-Index, Hive-TPC-DS-query6, Hive-TPC-DS-query7, Hive-TPC-DS-query9, Hive-TPC-DS-query13, Hive-TPC-DS-query12-2
3 Hive-Orderby, Hive-SelectQuery, Hive-TPC-DS-query8, Impala-SelectQuery, Hive-Crossproduct, Hive-Project, Hive-JoinQuery, Hive-AggregationQuery
4 Impala-TPC-DS-query6, Impala-TPC-DS-query12_2, Hive-TPC-DS-query3,Spark-NaiveBayes, Impala-TPC-DS-query7, Impala-TPC-DS-query13, Impala-TPC-DS-query9, Impala-TPC-DS-query10, Impala-TPC-DS-query3
5 Shark-Union, Spark-WordCount, Shark-Aggregation-AVG, Shark-Filter, Shark-Aggregation-MAX, Shark-SelectQuery, Shark-Aggregation-MIN, Shark-Aggregation-SUM
6 Impala-Filter, Impala-Aggregation-AVG, Impala-Union, Impala-Orderby, Impala-Aggregation-MAX, Impala-Aggregation-MIN, Impala-Aggregation-SUM,
7 Hive-Aggregation-AVG, Hive-Aggregation-MIM, Hive-AggregationSUM, Hadoop-Grep, Hive-Union, Hive-AggregationMAX, Hive-Filter, Hadoop-Pagerank
8 Shark-TPC-DS-query9, Shark-TPC-DS-query7, Shark-TPC-DS-query10, Shark-TPC-DS-query3
9 Shark-AggregationQuery, Shark-TPC-DS-query6, Shark-Project, Shark-TPC-DS-query13
10 Shark-JoinQuery, Shark-Orderby, Shark-Crossproduct
11 Spark-Kmeans
12 Shark-TPCDS-query8
13 Spark-Pagerank
14 Spark-Grep
15 Hadoop-WordCount
16 Hadoop-NaiveBayes
17 Spark-Sort

5. Representative Workloads Selection

There are two methods to choose the representative workload from each cluster.The first is to choose the workload that is as close as possible to the center of the cluster it belongs to. The other is to select an extreme workload situated at the “boundary” of each cluster.

Combined with hierarchical clustering result, we select the workload situated at the “boundary” of each cluster as the representative workload. The rationale behind the approach would be that the behavior of the workloads in the middle of a cluster can be extracted from the behavior of the boundary, for example through interpolation. So the representative workloads are listed in Table II. And the number of workloads that each selected workload represents is given in the third column.

Table II Treat the marginal ones as representative workloads

Workload name Number of workloads in its cluster
1 Cloud-OLTP-Read 10
2 Hive-Difference 9
3 Impala-SelectQuery 9
4 Hive-TPC-DS-query3 9
5 Spark-WordCount 8
6 Impala-Orderby 7
7 Hadoop-Grep 7
8 Shark-TPC-DS-query10 4
9 Shark-Project 4
10 Shark-Orderby 3
11 Spark-Kmeans 1
12 Shark-TPC-DS-query8 1
13 Spark-Pagerank 1
14 Spark-Grep 1
15 Hadoop-WordCount 1
16 Hadoop-NaiveBayes 1
17 Spark-Sort 1


Here we provide the package that only includes the workloads in Table II, so that the users can download the subset and deploy them on their own platform. The package and user manual can be found through the following links:

Workloads in Table II  download:  Marginal_ones_as_representatives.tar [User Manual]:PDF

In the case that researchers need the workloads which are chosen by the first method, i.e. choosing the workload that is as close as possible to the center of the cluster, we also list them in Table III.


Table III Treat the central ones as representative workloads

Workload name Number of workloads in its cluster
1 Cloud-OLTP-Write 10
2 Hive-TPC-DS-query13 9
3 Hive-AggregationQuery 9
4 Impala-TPC-DS-query6 9
5 Shark-Union 8
6 Impala-Aggregation-MAX 7
7 Hive-Aggregation-AVG 7
8 Shark-TPC-DS-query7 4
9 Shark-TPC-DS-query6 4
10 Shark-Crossproduct 3
11 Spark-Kmeans 1
12 Shark-TPCDS-query8 1
13 Spark-Pagerank 1
14 Spark-Grep 1
15 Hadoop-WordCount 1
16 Hadoop-NaiveBayes 1
17 Spark-Sort 1

The following links also provide the applications listed in Table III for researchers to download.

Workloads in Table III download:  Central_ones_as_representatives.tar [User Manual]:PDF

Simulator version

The workloads listed in Table II can representative the 77 workloads in BigDataBench. So to facilitate micro-architectural simulation, we deploy the 17 representative applications listed on Simics and MARSSx86, we also provide gem5 version in Hadoop standalone mode. We then provide the simulator images of these three full system simulators for researchers to download.

The workloads in BigDataBench are all distributed workloads using big data software stacks such as Hadoop, Spark and etc. Those workloads are running on a cluster, which consists of a master and several slaves nodes. The master node is responsible for distributing  tasks, while the slave nodes are executing the tasks. We simulate a two-node cluster (one master and one slave), and we provide both images. Users should boot up both the images and submit the job on the master node. For the slave node is responsible for processing the whole job, if users want to get some performance data, slave node should  be focused on. 

User manuals:  [BigDataBench Simulator Version] [BigDataBench GEM5 Version]

Simics Images download:  Master-image, Slave-image

MARSSx86 Images download: Master-image-hadoop, Slave-image-hadoop. Master-image-impala, Slave-image-impala

MARSSx86 Network Config Scripts package download: MARSS-network-script

Gem5 Images download: BigDataBench-gem5.img, linux-bigswap2.imgvmlinux-22-22-64



For Citations

If you need a citation for the BigDataBench architecture subset or the simulator version, please cite the following papers related with your work:

BigDataBench: a Big Data Benchmark Suite from Internet Services. [PDF]

Lei Wang, Jianfeng Zhan, ChunjieLuo, Yuqing Zhu, Qiang Yang, Yongqiang He, WanlingGao, Zhen Jia, Yingjie Shi, Shujie Zhang, Cheng Zhen, Gang Lu, Kent Zhan, Xiaona Li, and BizhuQiu. The 20th IEEE International Symposium On High Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando, Florida, USA.

Characterizing and Subsetting Big Data Workloads. [PDF]

Zhen Jia, Jianfeng Zhan, Wang Lei, Rui Han, Sally A. McKee, Qiang Yang, Chunjie Luo, and Jingwei Li.  In 2014 IEEE International Symposium on Workload Characterization (IISWC 2014).