DRAMsim3 models the timing paramaters and memory controller behavior for several DRAM protocols such as DDR3, DDR4, LPDDR3, LPDDR4, GDDR5, GDDR6, HBM, HMC, STT-MRAM. It is implemented in C++ as an objected oriented model that includes a parameterized DRAM bank model, DRAM controllers, command queues and system-level interfaces to interact with a CPU simulator (GEM5, ZSim) or trace workloads. It is designed to be accurate, portable and parallel.

If you use this simulator in your work, please consider cite:

[1] S. Li, Z. Yang, D. Reddy, A. Srivastava and B. Jacob, "DRAMsim3: a Cycle-accurate, Thermal-Capable DRAM Simulator," in IEEE Computer Architecture Letters. Link

See Related Work for more work done with this simulator.

This simulator by default uses a CMake based build system. The advantage in using a CMake based build system is portability and dependency management. We require CMake 3.0+ to build this simulator. If cmake-3.0 is not available, we also supply a Makefile to build the most basic version of the simulator.

Doing out of source builds with CMake is recommended to avoid the build files cluttering the main directory.

# cmake out of source build
mkdir build
cd build
cmake ..

# Build dramsim3 library and executables
make -j4

# Alternatively, build with thermal module enabled
cmake .. -DTHERMAL=1

The build process creates dramsim3main and executables in the build directory. By default, it also creates libdramsim3.so shared library in the project root directory.

# help
./build/dramsim3main -h

# Running random stream with a config file
./build/dramsim3main configs/DDR4_8Gb_x8_3200.ini --stream random -c 100000 

# Running a trace file
./build/dramsim3main configs/DDR4_8Gb_x8_3200.ini -c 100000 -t sample_trace.txt

# Running with gem5
--mem-type=dramsim3 --dramsim3-ini=configs/DDR4_4Gb_x4_2133.ini

The output can be directed to another directory by -o option or can be configured in the config file. You can control the verbosity in the config file as well.

scripts/plot_stats.py can visualize some of the output (requires matplotlib):

# generate histograms from overall output
python3 scripts/plot_stats dramsim3.json

# or
# generate time series for a variety stats from epoch outputs
python3 scripts/plot_stats dramsim3epoch.json

Currently stats from all channels are squashed together for cleaner plotting.

Gem5 integration: works with a forked Gem5 version, see https://github.com/umd-memsys/gem5 at dramsim3 branch for reference.

SST integration: see https://git.ece.umd.edu/shangli/sst-elements/tree/dramsim3 for reference. We will try to merge to official SST repo.

ZSim integration: see https://git.ece.umd.edu/shangli/zsim/tree/master for reference.

├── configs                 # Configs of various protocols that describe timing constraints and power consumption.
├── ext                     # 
├── scripts                 # Tools and utilities
├── src                     # DRAMsim3 source files
├── tests                   # Tests of each model, includes a short example trace
├── CMakeLists.txt
├── Makefile
├── LICENSE
└── README.md

├── src  
    bankstate.cc: Records and manages DRAM bank timings and states which is modeled as a state machine.
    channelstate.cc: Records and manages channel timings and states.
    command_queue.cc: Maintains per-bank or per-rank FIFO queueing structures, determine which commands in the queues can be issued in this cycle.
    configuration.cc: Initiates, manages system and DRAM parameters, including protocol, DRAM timings, address mapping policy and power parameters.
    controller.cc: Maintains the per-channel controller, which manages a queue of pending memory transactions and issues corresponding DRAM commands, 
                   follows FR-FCFS policy.
    cpu.cc: Implements 3 types of simple CPU: 
            1. Random, can handle random CPU requests at full speed, the entire parallelism of DRAM protocol can be exploited without limits from address mapping and scheduling pocilies. 
            2. Stream, provides a streaming prototype that is able to provide enough buffer hits.
            3. Trace-based, consumes traces of workloads, feed the fetched transactions into the memory system.
    dram_system.cc:  Initiates JEDEC or ideal DRAM system, registers the supplied callback function to let the front end driver know that the request is finished. 
    hmc.cc: Implements HMC system and interface, HMC requests are translates to DRAM requests here and a crossbar interconnect between the high-speed links and the memory controllers is modeled.
    main.cc: Handles the main program loop that reads in simulation arguments, DRAM configurations and tick cycle forward.
    memory_system.cc: A wrapper of dram_system and hmc.
    refresh.cc: Raises refresh request based on per-rank refresh or per-bank refresh.
    timing.cc: Initiate timing constraints.

First we generate a DRAM command trace. There is a CMD_TRACE macro and by default it's disabled. Use cmake .. -DCMD_TRACE=1 to enable the command trace output build and then whenever a simulation is performed the command trace file will be generated.

Next, scripts/validation.py helps generate a Verilog workbench for Micron's Verilog model from the command trace file. Currently DDR3, DDR4, and LPDDR configs are supported by this script.

Run

./script/validataion.py DDR4.ini cmd.trace

To generage Verilog workbench. Our workbench format is compatible with ModelSim Verilog simulator, other Verilog simulators may require a slightly different format.

[1] Li, S., Yang, Z., Reddy D., Srivastava, A. and Jacob, B., (2020) DRAMsim3: a Cycle-accurate, Thermal-Capable DRAM Simulator, IEEE Computer Architecture Letters.

[2] Jagasivamani, M., Walden, C., Singh, D., Kang, L., Li, S., Asnaashari, M., ... & Yeung, D. (2019). Analyzing the Monolithic Integration of a ReRAM-Based Main Memory Into a CPU's Die. IEEE Micro, 39(6), 64-72.

[3] Li, S., Reddy, D., & Jacob, B. (2018, October). A performance & power comparison of modern high-speed DRAM architectures. In Proceedings of the International Symposium on Memory Systems (pp. 341-353).

[4] Li, S., Verdejo, R. S., Radojković, P., & Jacob, B. (2019, September). Rethinking cycle accurate DRAM simulation. In Proceedings of the International Symposium on Memory Systems (pp. 184-191).

[5] Li, S., & Jacob, B. (2019, September). Statistical DRAM modeling. In Proceedings of the International Symposium on Memory Systems (pp. 521-530).

[6] Li, S. (2019). Scalable and Accurate Memory System Simulation (Doctoral dissertation).