The learning objectives for Pugnacious Profiles are:
- Understanding profiling
- Performance optimization (both time and memory)
- Applying data structures in systems
Over the course of this assignment, we will be exploring how profilers can be used to optimize your C program through the combination of dynamic analysis, data-structures and algorithms along with systems programming.
Note: We recommend using the EWS systems instead of the VM as there would be further discrepancies of metrics and compatibility between both.
High Frequency Trading #
In high-frequency trading (HFT), programs analyze market data to find and take advantage of trading opportunities that often only exist for a few seconds. Messages describing trade offers can reach rates of up to 250,000 messages/second. Thus, programs involved in automated trading have to process a lot of data extremely quickly.
The program provided to you will read in and process a stream of order messages for multiple stocks. These messages will be used to update our program’s view of the market state. This state is known as the “order book”. The messages we process will either trigger the entry of a new order, change an existing order, or delete an order. At the end of the stream, this program will print out all the current orders in the order book.
Input / Output #
The program will check the command line arguments for a -i input filename flag. If it is present, the program will read the input stream from the file named there. If it is not, the program will read the input from standard input. Likewise, it will check the command line arguments for a -o output filename flag. If it is present, the program will write the output stream to the file named there. If it is not, the program will write the output to standard output.
The program will read in input as ASCII text, of which the format is as follows:
A <id> <side> <symbol> <quantity> <price> - A new order is added with the supplied details. Side is either B for buy or S for sell
X <id> <symbol> - An order is cancelled
T <id> <symbol> <quantity> - An order is (partially) executed for the given quantity (remove this quantity from the existing order in your records)
C <id> <symbol> <quantity> - An order is (partially) cancelled for the given quantity (remove this quantity from the existing order in your records)
R <id> <symbol> <quantity> <price> - An order is changed to have the given price and quantity
A 344532111 S SPY 300 117.880000 R 344532111 SPY 300 117.840000 T 344532111 SPY 100 C 344532111 SPY 100 A 344533172 B SPY 200 117.110000 A 344533348 B SPY 280 118.050000 X 344533348 SPY
This would be a new order to sell 300 shares of the stock SPY at $117.88. It is followed by a message to change the previous order to sell at a price of $117.84. The next two messages indicate that the order is partially executed and cancelled by 100 respectively. The next message is a new order to buy 200 shares of the stock SPY at $117.11. The last two messages add a buy order and then cancel it. If this was the entire message stream, your program would print out the following output.
344533172 B 200 117.110000 344532111 S 100 117.840000
Part - 1 #
High frequency trading requires optimal performance demanding high speed and for this part of the project we are going
to meet those goals. While the program provided to you does everything correctly, it is too slow to scale up and process
real-time orders. On the other hand your competitors solution (TA reference implementation) is way faster than this.
Your job is to analyze the performance of this program and bring it within a certain threshold thus defeating your competitor.
To measure time, use the /usr/bin/time command:
$ time ./orderbook -i input/ascii_data.txt -o ob.txt
On the EWS systems in Siebel, the implementation provided to you will roughly run around the following time output:
real 0m8.281s user 0m8.022s sys 0m0.042s
Note: While this may be higher or lower based on the computational resources available, it will never get close to the expected performance threshold.
What do each of these values mean and which one is relevant for us?
Your job is to defeat our reference implementation (your competitors) which has a user time of 2.000 seconds and a system time of 0.020 seconds for the data-set of ascii_data.txt provided to you. Your program when executed with -h flag needs to be below this threshold maintaining the correctness in order to complete part 1.
While the time command does provide us with an overall performance of our program, it doesn’t help pin point where the issue is. Gprof will come to our rescue. To utilize gprof, use the following commands:
$ make orderbook_profile $ ./orderbook_gprof -i input/ascii_data.txt -o ob.txt $ gprof orderbook_gprof gmon.out > analysis.txt $ cat analysis.txt
Examine the analysis.txt file which will break down the performance of the program as per the respective functions which should help you get started.
Note: The performance with gprof can be a little more due to the overhead. We will be testing your program with the
time command which should provide more accurate measures.
- Think whether the issue is inside one particular function or the underlying data-structure.
- Theoretical time complexity ~ Computational time. Is there any particular type of data-structure well suited for this problem which can provide a near constant time complexity.
- What contributes to the system time.
Part - 2 (Optional) #
While time is an important factor while profiling, memory goes hand in hand with it. Once you have optimized for time, you need to now bring your heap memory equal to or less than our reference implementation. To measure memory use the following command:
$ make orderbook_profile $ env LD_PRELOAD=/lib/libmemusage.so ./orderbook_mem -i input/ascii_data.txt -o ob.txt
This should provide you with the following output regarding the amount of heap memory consumed:
Memory usage summary: heap total: 16948324, heap peak: 2519164, stack peak: 344 total calls total memory failed calls malloc| 470778 16948324 0 realloc| 0 0 0 (nomove:0, dec:0, free:0) calloc| 0 0 0 free| 548400 16948324
Note: orderbook_mem is compiled using -m32 flag which generates a 32 bit executable. This will thus generate consistent memory usage on both 32 bit and 64 bit systems.
Our reference implementation uses 11320000 bytes of heap memory for ascii_data.txt dataset. For the -h flag which will execute your time optimized implementation, bring your memory consumption equal to or less than this value. We will be looking out for the stack segment as well and thus ensure that your stack peak is less than 2500 bytes.
- Go over the data inside ascii_data.txt determining the highest values. We will not be crossing that threshold for our testing data-sets. Typically, in the real world scenario’s there is a predefined min / max however you will also come across instances when you have to come up with a comprehensive min-max values and store it appropriately.
- Think about the respective data-types that can effectively store the values without wasting memory.
- Take into consideration padding. Memory is stored and retrieved in the form of words size of which is 4 bytes for 32-bit and 8 bytes for 64-bit. Thus, size of 29 byte struct will be padded to 32 bytes. Struct Padding & Packing
Challenge (Not Graded) #
Once you have optimized your program for both time and memory, your implementation is ready to face the real world challenge. We have a data file with 10 million order entries. Do you think your program can handle so many transactions?
Please click on the following link to download the data file (~225 MB): dat10m.txt
Our reference implementation which uses no tricks and no clever optimizations except for allocating more memory for our datastructure executes this with the following performance:
$ time ./orderbook -h -i input/dat10m.txt -o ob.txt real 0m5.574s user 0m5.127s sys 0m0.095s
It is very well possible to get below ~1.000s for processing this dataset. Up for a challenge!
Submission Instructions #
Please read details on Academic Integrity fully. These are shared by all assignments in CS 241.
We will be using GitHub as our hand-in system this semester. Our grading system will checkout your most recent (pre-deadline) commit for grading. Therefore, to hand in your code, all you have to do is commit and push to your Github repository.
To check out the provided code for
assignments-pugnacious_profilers from the class repository, go to your cs241 directory (the one you checked out for “know your tools”) and run:
git pull release master
If you run
ls you will now see a
assignments-pugnacious_profilers folder, where you can find this assignment! To commit your changes (send them to us), type:
git add assignments-pugnacious_profilers git commit -m "assignments-pugnacious_profilers submission" git push origin master
Your repository directory can be viewed from a web browser from the following URL: https://github-dev.cs.illinois.edu/cs241-fa20/NETID/tree/master/assignments-pugnacious_profilers where NETID is your University NetID. It is important to check that the files you expect to be graded are present and up to date in your remote git copy.
Assignment Feedback #
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