Multithreaded application workload

To measure the effects of Hyper-Threading on Linux multithreaded applications, we use the chat benchmark, which is modeled after a chat room. The benchmark includes both a client and a server. The client side of the benchmark will report the number of messages sent per second; the number of chat rooms and messages will control the workload. The workload creates a lot of threads and TCP/IP connections, and sends and receives a lot of messages. It uses the following default parameters:

• Number of chat rooms = 10
• Number of messages = 100
• Message size = 100 bytes
• Number of users = 20

By default, each chat room has 20 users. A total of 10 chat rooms will have 20x10 = 200 users. For each user in the chat room, the client will make a connection to the server. So since we have 200 users, we will have 200 connections to the server. Now, for each user (or connection) in the chat room, a "send" thread and a "receive" thread are created. Thus, a 10-chat-room scenario will create 10x20x2 = 400 client threads and 400 server threads, for a total of 800 threads. But there's more.

Each client "send" thread will send the specified number of messages to the server. For 10 chat rooms and 100 messages, the client will send 10x20x100 = 20,000 messages. The server "receive" thread will receive the corresponding number of messages. The chat room server will echo each of the messages back to the other users in the chat room. Thus, for 10 chat rooms and 100 messages, the server "send" thread will send 10x20x100x19 or 380,000 messages. The client "receive" thread will receive the corresponding number of messages.

The test starts by starting the chat server in a command-line session and the client in another command-line session. The client simulates the workload and the results represent the number of messages sent by the client. When the client ends its test, the server loops and accepts another start message from the client. In our measurement, we ran the benchmark with 20, 30, 40, and 50 chat rooms. The corresponding number of connections and threads are shown in Table 3.

Table 3. Number of chat rooms and threads tested

Table 4 show the performance impact of Hyper-Threading on the chat workload. Each data point represents the geometric mean of five runs. The data set clearly indicates that Hyper-Threading could improve the workload throughput from 22% to 28% depending on the number of chat rooms. Overall, Hyper-Threading will boost the chat performance by 24% based on the geometric mean of the 4 chat room samples.

Table 4. Effects of Hyper-Threading on chat throughput

Figure 1. Effects of Hyper-Threading on the chat workload