1. Summary of Semeru

Semeru is a managed runtime built for a memory disaggregated cluster where each managed application uses one CPU server and multiple memory servers. When launched on Semeru, the process runs its application code (mutator) on the CPU server, and the garbage collector on both the CPU server and memory servers in a coordinated manner. Due to task ofﬂoading and moving computation close to data, Semeru signiﬁcantly improves the locality for both the mutator and GC and, hence, the end-to-end performance of the application. Please refer to our OSDI'20 paper Semeru: A Memory-Disaggregated Managed Runtime for more details.

Except for the stable version, We also maintain an active development version repo, Semeru-dev.

2. Setup environments

Hardware: Intel servers with InﬁniBand
Kernel environments: Linux-4.11-rc8
OS versions: CentOS 7.5(7.6) with MLNX-OFED 4.3(4.5), Ubuntu 18.04 with MLNX 4.7
Run-time environments: OpenJDK 12.02
GNU environments: GCC 4.8 to GCC 5.5, GLIBC 2.27
Code licenses: The GNU General Public License (GPL)

3. Description

3.1 Semeru's Codebase

Semeru contains the following three components:

the Linux kernel, which includes a modiﬁed swap system, block layer and a RDMA module
the CPU-server Java Virtual Machine (JVM)
the Memory-server lightweight Java Virtual Machine (LJVM)

These three components and their relationships are illustrated in figure below:

3.2 Deploying Semeru

To build Semeru, the ﬁrst step is to download its source code:

git clone https://github.com/uclasystem/Semeru.git
# if the repo name is different, e.g. `Semeru-dev`, change it to `Semeru` to easily run following commands
mv Semeru-dev Semeru

When deploying Semeru, install the three components in the following order: the kernel on the CPU server, the Semeru JVM on the CPU server, and the LJVM on each memory server. Finally, connect the CPU server with memory servers before running applications.

CPU Server Kernel Installation

We ﬁrst discuss how to build and install the kernel.

Modify grub and set transparent_hugepage to madvise:

sudo vim /etc/default/grub 
+ transparent_hugepage=madvise

Modify Semeru/linux-4.11-rc8/include/linux/swap_global_struct.h as desired, e.g.,

#define ENABLE_SWP_ENTRY_VIRT_REMAPPING 1 ->
//#define ENABLE_SWP_ENTRY_VIRT_REMAPPING 1
#define RDMA_DATA_REGION_NUM 2UL  // default 32GB ->
#define RDMA_DATA_REGION_NUM 8UL  // default 32GB

// Scatter/Gather support
// Please confirm the max number of scatter-gather packets supported by your InfiniBand
// and update the macro MAX_REQUEST_SGL, defined in include/linux/swap_global_struct.h
// If you are not sure the exact number of the MAX_REQUEST_SGL, please set it to 3.

#define MAX_REQUEST_SGL      32

Install the kernel and restart the machine on both CPU server and memory servers:

cd ~/Semeru/linux-4.11-rc8
sudo ./build_kernel.sh build
# `grub_boot_verion` may need to be changed to boot with the correct kernel
# use `uname -a` after reboot to see whether the correct kernel is booted
sudo ./build_kernel.sh install
sudo reboot

Install the MLNX-OFED driver. We download the MLNX_OFED_LINUX-4.5-1.0.1.0-rhel7.6-x86_64, and install it against our newly built kernel:

# check CentOS version, and select the correct driver to install!
# Download link can be find here: https://www.mellanox.com/products/infiniband-drivers/linux/mlnx_ofed
wget https://content.mellanox.com/ofed/MLNX_OFED-4.5-1.0.1.0/MLNX_OFED_LINUX-4.5-1.0.1.0-rhel7.6-x86_64.tgz
tar xzf MLNX_OFED_LINUX-4.5-1.0.1.0-rhel7.6-x86_64.tgz
cd MLNX_OFED_LINUX-4.5-1.0.1.0-rhel7.6-x86_64
sudo yum install -y createrepo rpm-build pciutils gtk2 atk cairo libxml2-python tcsh lsof tcl tk net-tools
sudo ./mlnxofedinstall --add-kernel-support
sudo /etc/init.d/openibd restart

After installing the OFED driver, please confirm the RDMA works well between the CPU server and memory servers.

Build and install Semeru RDMA module

# Configure the number and memory resources of memory servers
# Take 2 memory servers as example
# Set the MACRO variables in ~/Semeru/linux-4.11-rc8/include/linux/swap_global_struct.h
# Adjust the number of memory servers and the Regions will be divided into each memory server evenly

#define NUM_OF_MEMORY_SERVER 2UL

# And then, each memory server contains 1 Meta Region and 4 Data Regions.

# Add the IP of each memory server into
# ~/Semeru/linux-4.11-rc8/semeru/semeru_cpu.c
# e.g., the Inﬁniband IPs of the 2 memory servers are 10.0.0.2 and 10.0.0.4
char *mem_server_ip[] = {"10.0.0.2","10.0.0.4"};
uint16_t mem_server_port = 9400;

# Then build the Semeru RDMA module
cd ~/Semeru/linux-4.11-rc8/semeru
make

Install JDK build dependencies

To build JDK on all servers, some packages needs to be installed first:

sudo yum groupinstall "Development Tools" -y
sudo yum install libXtst-devel libXt-devel libXrender-devel libXrandr-devel libXi-devel cups-devel fontconfig-devel alsa-lib-devel -y

Install the CPU-Server JVM

We next discuss the steps to build and install the CPU-server JVM.

Download Oracle JDK 12 to build Semeru JVM

# Assume jdk 12.0.2 is under path: ${home_dir}/jdk-12.0.2 
# Or change the path in shell script, ~/Semeru/CPU-Server/build_cpu_server.sh
code ~/Semeru/CPU-Server/build_cpu_server.sh
boot_jdk="${home_dir}/jdk-12.0.2"

Build the CPU-server JVM

# ${build_mode} can be one of the three modes:
# slowdebug, fastdebug, or release.
# We recommend slowdebug mode to debug the JVM code 
# and release mode to test the performance.
# Please make sure both the CPU server and 
# memory servers use the same build mode.
cd ~/Semeru/CPU-Server/
./build_cpu_server.sh ${build_mode}
./build_cpu_server.sh release
./build_cpu_server.sh build
# Take release mode as example — the compiled JVM will be in:
# ~/Semeru/CPU-Server/build/linux-x86_64-server-release/jdk

Install the Memory-Server LJVM

The next step is to install the LJVM on each memory server.

Download OpenJDK 12 and build the LJVM

# Assume OpenJDK12 is under the path: ${home_dir}/jdk-12.0.2
# Or you can change the path in the script  
# ~/Semeru/Memory-Server/build_memory_server.sh
boot_jdk="${home_dir}/jdk-12.0.2"

Change the IP addresses

// E.g., mem-server #0’s IP is 10.0.0.2, memory server ID is 0.
// Change the IP address and ID in ﬁle:
// ~/Semeru/Memory-Server/src/hotspot/share/utilities/globalDefinitions.hpp
// @Mem-server #0
#define NUM_OF_MEMORY_SERVER 2
#define CUR_MEMORY_SERVER_ID 0
static const char cur_mem_server_ip[] = "10.0.0.2";
static const char cur_mem_server_port[]= "9400";

Build and install the LJVM

# Use the same ${build_mode} as the CPU-server JVM.
cd ~/Semeru/Memory-Server/
./build_memory_server.sh ${build_mode}
./build_memory_server.sh build
# the JDK is under now under ${home_dir}/Semeru/Memory-Server/build/linux-x86_64-server-${build_mode}/jdk

# optional: install JDK. If installed,
# The compiled Java home will be installed under:
# ${home_dir}/jdk12u-self-build/jvm/openjdk-12.0.2-internal
./build_memory_server.sh install

## Set JAVA_HOME to point to the folder.

Running Applications

To run applications, we ﬁrst need to connect the CPU server with memory servers. Next, we mount the remote memory pools as a swap partition on the CPU server. When the application uses more memory than the limit set by cgroup, its data will be swapped out to the remote memory via RDMA.

Launch memory servers

# Use the shell script to run each memory server.
# ${execution_mode} can be execution or gdb.
# @Each memory server
cd ~/Semeru/ShellScript
## maybe add `JAVA_HOME="${home_dir}/Semeru/Memory-Server/build/linux-x86_64-server-${build_mode}/jdk"` in run_rmem_server_with_rdma_service.sh
code run_rmem_server_with_rdma_service.sh
sudo yum install numactl -y
## config SEMERU_HOME in .bash_rc
tmux
./run_rmem_server_with_rdma_service.sh Case1 execution

Connect the CPU server with memory servers

# @CPU server
# The default size of remote memory server is 36GB:
# 4GB meta region and 32GB data regions.
# If not, assign the data regions size to the parameter 
# in ~/Semeru/ShellScript/install_semeru_module.sh :
# SWAP_PARTITION_SIZE="32G"
# We don't recommend to change the Java heap size right now.
# Please refer to the Known Issues chapter for more details.
cd ~/Semeru/ShellScript/
## modifiy `home_dir` in install_semeru_module.sh
code install_semeru_module.sh
./install_semeru_module.sh semeru
# To close the swap partition, do the following:
# @CPU server
cd ~/Semeru/ShellScript/
./install_semeru_module.sh close_semeru
# If the memory servers are crashed, the CPU server should disconnect 
# with the memory servers automatically.
# In this case, we recommend to restart the CPU server for performance test.
# Because the crash of memory servers may cause kernel memory leak of CPU server.

Set a CPU server cache size limit for an application

# E.g., Create a cgroup with 10GB memory limitation.
# The shellscript will create a cgroup, named as memctl.
# When setting the CPU server local cache, please leave enough size for the native memory.
# Refer to the Known Issues chapter for more ditals.
# @CPU server
cd ~/Semeru/ShellScript

# parameters <create/delete>  <cgroup_name> <memory size>
./cgroupv1_manage.sh create spark 9g
# Or delete the cgroup
./cgroupv1_manage.sh delete spark

Add a Spark executor into the created cgroup

# Add a Spark worker into the cgroup, memctl.
# Its sub-process, executor, falls into the same cgroup.
# Modify the function *start_instance* under
# Spark/sbin/start-slave.sh
# @CPU server
cgexec --sticky -g memory:memctl "${SPARK_HOME}/sbin" /sparkdaemon.sh start $CLASS $WORKER_NUM -webui-port "$WEBUI_PORT" $PORT_FLAG $PORT_NUM $MASTER "$@"
# We also recommend that only run the executor on the CPU-Server JVM.
# Please refer to the FAQ chapter for more details.
# In order to achive this, specify the executor JVM in Spark/conf/spark-defaults.conf :
spark.executorEnv.JAVA_HOME=${semeru_cpu_server_jvm_dir}/jdk

# More explanation
# 1) We recommend to only add a single process into the cgroup.
# E.g., Only add the Spark Executor into process, but not adding the worker process.
# 1.1) Keep the Spark/sbin/start-slave.sh unmodified
# 1.2) Run the sh Semeru/ShellScript/cgroupv1_add_executor.sh before launch the Spark app.

# 2) We recommend to reserve core#0 for Control Path 
# E.g., modify the spark/sbin/start-slave.sh to use core #1 to #15 only:
taskset -c 1-15  "${SPARK_HOME}/sbin"/spark-daemon.sh start $CLASS $WORKER_NUM \
     --webui-port "$WEBUI_PORT" $PORT_FLAG $PORT_NUM $MASTER "$@"

Launch a Spark application

Some Semeru JVM options need to be added for both CPU-server JVM and LVJMs. CPU-server JVM and memory server LJVMs should use the same value for the same JVM option.

# E.g., under the conﬁguration of 25% local memmory
# 512MB Java heap Region
# @CPU server
-XX:+SemeruEnableMemPool -XX:EnableBitmap -XX:-UseCompressedOops -Xnoclassgc -XX:G1HeapRegionSize=512M -XX:MetaspaceSize=0x10000000 -XX:SemeruLocalCachePercent=25
# @Each memory server
# ${MemSize}: the memory size of current memory server
# ${ConcThread}: the number of concurrent threads
-XX:SemeruEnableMemPool -XX:-UseCompressedOops -XX:SemeruMemPoolMaxSize=${MemSize} -XX:SemeruMemPoolInitialSize=${MemSize} -XX:SemeruConcGCThreads=${ConcThread}
# We provide some shellscript examples for Spark applications under the directory: Semeru/ShellScrip/SparkAppShellScrip
# Please check their JVM parameters.

FAQ

Semeru is an academic proterotype to show the benefits of managing data on disaggreagted datacenters cross-layers. It does have some limitations and we will keep updating the code of Semeru. If you encounter any problems, please open an issue or feel free to contact us:

Chenxi Wang [email protected];

Haoran Ma [email protected].

1. How many JVMs can run on the CPU server ?

At this moment, only one JVM can run on the CPU server. When launch the Spark cluster, multiple daemon processes run in the backgroud, e.g., Worker, Executors. Each process is a separate JVM process. The Worker process is used for management and the Executor process is used for real computation. Please only let the Executor process runs on Semeru CPU server JVM. One Executor per CPU server.

2. Which part of data can be swapped out to memory servers ?

Part of the Meta space and all the Data space (Java heap) can be swapped out to memory servers via the data path. Some meta variables can't be swapped out to memory servers, even they are used as the purpose on both the CPU server and memory servers. Because these variables contain virtual functions. For the same object instance, the virtual functions' addresses are usually different in the CPU server and memory servers. Swapping out these objects instances from CPU server to memory servers are not safe.

Known Issues

We have found some unfixed issues. Some of them are potential optimizations that can be applied. Some of them are potential bugs. We will fix them and update the code latter.

1. Using too much native memory can cause Out-Of-Memory error.

In our design, only part of the Meta space and the Data space (Java heap) can be swapped out to memory servers. If the Java application uses too much native memory which exceeds the CPU server local capacity, the process will be killed by the Out-Of-Memory error. We will add a dedicated remote memory pool on the memory servers for the native memory space later.

2. Some meta data in CPU server JVM can be freed.

In order to do concurrent tracing, G1 GC maintains some large data structures, e.g., the bitmap. Its size can reach up to 1/32 of the Java heap size. Semeru moved all the concurrent tracing to memory servers. There is no need to keep these meta data structures on the CPU server JVM. Removing them can save both time and space overhead. Warning : Please reserve 4GB CPU server memory for the meta regions when create the cgroup. We will fix this problem later.

3. Java heap size is fixed at 32GB, Start at 0x400,100,000,000.

Some meta variables are related to the Java heap size. E.g., the CPU server swap file/partition size, memory servers' alive_bitmap size etc. It's a little hard to change the Java heap size right now. We will update a new version to fix this problem later.

4. Do NOT use update-alternatives to configure the gcc/g++/bnu tools.

Or the linker can NOT find the ofed libraries, under /usr/lib, successfully. This can lead to link errors, such as undefined reference to `ibv_dereg_mr'.

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