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Fast, Flexible and Comprehensive Bug Detection for Persistent Memory Programs

Bang Di, Jiawen Liu, Hao Chen, Dong Li
The International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2021

Table of Contents

• Introduction to PMDebugger
• System configurations
  • Hardware Dependencies
  • Software Dependencies
• Installation
  • Build PMDebugger
  • Build PMDK
  • Build Redis
  • Build Memcached
• Evaluation
  • Performance
  • Bug Detection Capability
  • New Bugs Found By PMDebugger
• Experiment Customization

Introduction to PMDebugger

Debugging PM programs faces a fundamental tradeoff between performance overhead and bug coverage (comprehensiveness). Large performance overhead or limited bug coverage makes debugging infeasible or ineffective for PM programs. In this paper, we propose PMDebugger, a debugger to detect crash consistency bugs. Unlike prior work, PMDebugger is fast, flexible and comprehensive for bug detection. The design of PMDebugger is driven by the characterization of how three fundamental operations in PM programs (store, cache writeback and fence) typically happen in PM programs. PMDebugger uses a hierarchical design composed of PM debugging-specific data structures, operations and bug-detection algorithms (rules). We generalize nine rules to detect crash-consistency bugs for various PM persistency models. Compared with a state-of-the-art detector (XFDetector) and an industry-quality detector ( Pmemcheck), PMDebugger leads to 49.3x and 3.4x speedup on average. Compared with another state-of-the-art detector (PMTest) optimized for high performance, PMDebugger achieves comparable performance, without heavily relying on the programmer’s annotation but detect 38 more bugs than PMTest on ten applications. PMDebugger also identifies more bugs than XFDetector, Pmemcheck and PMTest. PMDebugger detects 19 new bugs in a real application (memcached) and two new bugs from Intel PMDK.

System configurations

Hardware Dependencies

PMdebugger debugs workloads that directly manage the persistent data on PM through a DAX file system. To debug these workloads, the hardware requires to provide a persistent memory device, either by using real PM (Intel Optane DC Persistent Memory) or emulating PM with DRAM.

Real PM system

• CPU: Intel 2nd Generation Xeon Scalable Processor (Gold or Platinum).
• Persistent Memory: Intel Optane DC Persistent Memory (at least 1x 128GB DIMM) and DDR4 RDIMM.

Please refer to Intel's guide to initialize DC persistent memory in App Direct mode. In the rest of this documentation, we assume the PM device is mounted on /mnt/dbpmemfs.

Emulated PM system

• CPU: Intel 1st/2nd Generation Xeon Scalable Processor.
• Memory: at least DDR4.

You can simply use /dev/shm as a persistent memory file system to debug workloads.

Software Dependencies

The following is a list of software dependencies for PMDebugger and workloads (the listed versions have been tested, other versions might work but not guaranteed):

• OS: Ubuntu 18.04, Linux kernel 5.0.
• Compiler: g++/gcc-9.2.
• Tool: Valgrind-3.15.
• Dependent libraries: libevent, libfabric, libseccomp, autoconf, pkg-config, libndctl-devel (v63 or later), libdaxctl-devel (v63 or later). They may require sudo privilege (such as sudo apt-get install xxx).

Installation

This repository is organized as the following structure:

• valgrind-pmdebugger/: The source code of our tool.
• pmdk/: Intel's PMDK library, including its example PM programs.
• memcached-pmem-master: A Memcached implementation (from Lenovo).
• memslap/: A tool to run Memcached.

You can also simply run scripts to build them: build_pmdebugger.sh, build_pmdk.sh, build_redis.sh and build_memcached.sh. Note that if you are not sure that any dependencies are ready, please build it step by step. The followings are the detailed instructions to build PMDebugger and workloads separately.

Build PMDebugger

$ cd valgrind-pmdebugger
$ ./autogen.sh
$ ./configure
$ make
$ make check
$ sudo make install 

Build PMDK

$ cd pmdk/
$ make
$ sudo make install (if you require setting environment variables to build, run sudo -E make install)

Build Redis

Redis depends on an old version PMDK, so you firstly need to build PMDK for Redis.

$ git clone https://github.com/pmem/redis.git -o redis
$ cd redis/
$ git checkout 3.2-nvml

$ cd deps
$ git clone https://github.com/pmem/pmdk.git
$ cd pmdk
$ git checkout tags/1.5.2
$ make

Then, build Redis.

$ cd ../../ (back to redis fold)
$ make USE_PMDK=yes STD=-std=gnu99
$ cp ../redis_run.sh  run.sh
$ cp ../redis.conf ./

Build Memcached

$ cd memcached-pmem-master
$ CFLAGS=-Wno-error ./configure --enable-pslab
$ make 

Evaluation

After building the PMDebugger suite, we can start testing performance and reproducing bugs. Note that you can directly get results (Figure 8 and Table 5) out of box. Please see here.

Performance

Tests for the following programs are available in PMDebugger:

• PMDK program examples:
  • btree
  • ctree
  • rbtree
  • hashmap_tx
  • hashmap_atomic
  • synth_strand (synthetic benchmark for the strand persistency model)
• Redis
• Memcached

We choose Pmemcheck for comparison, because it is an industry-quality detector based on Valgrind for instrumentation, while PMDebugger uses Valgrind too. We provide scripts for running programs. The detailed steps are as follows.

PMDK Examples

Use script pmdk/run.sh to run all those benchmarks. The usage is shown as follows.

Usage: ./run.sh <CHECKER> <INPUTSIZE> <WORKLOAD>
       CHECKER:   Debugger tool name (pmdebugger, pmemcheck, Nulgrind, and original). "original" represents original program with detector disabled.
       INPUTSIZE: The number of data insertions.
       WORKLOAD:  The workload to be tested.

For example, we insert 1024 elements in btree to evaluate the performance of PMDebugger, so we can run the following command:

$ ./run.sh pmdebugger 1024 btree

What to look at: PMDebugger reports all detected bugs (if any) after a test is complete. PMDK benchmarks report the execution time (i.e., total-avg).

Redis

Use script redis/run.sh to run the Redis example. The usage is shown as follows.

Usage: ./run.sh <CHECKER> <INPUTSIZE>
       CHECKER:    Debugger tool name (pmdebugger, pmemcheck, Nulgrind, and original). "original" represents original program with detector disabled.
       INPUTSIZE: The number of LRU tests.

For example, we use 100000 LRU tests in Redis to evaluate the performance of PMDebugger, so we can run the following command:

$ ./run.sh pmdebugger 100000

What to look at: PMDebugger reports all detected bugs (if any) after a test is complete. Redis benchmarks report the throughput (i.e., Gets/sec).

Memcached

We use memslap in WHISPER to run Memcached. You can use the script memslap/run.sh to run Memcached examples. The usage is shown as follows.

Usage: ./run.sh <CHECKER> <INPUTSIZE>
       CHECKER:    Debugger tool name (pmdebugger, pmemcheck, Nulgrind, and original). "original" represents original program with detector disabled.
       INPUTSIZE: The number of operations to execute.

For example, we execute 10000 operations in Memcached to evaluate the performance of PMDebugger, so we can run the following command:

$ ./run.sh pmdebugger 10000

What to look at: PMDebugger reports all detected bugs (if any) after a test is complete. Memcached benchmarks report the execution time (i.e., Run time:).

Simplified performance scripts

I simplify the output of the above version (run.sh) and you can directly get the result (such as the execution and the throughput). You only require to replace run.sh with brief_run.sh. For example, we use 100000 LRU tests in Redis to evaluate the performance of PMDebugger, so we can run the following command:

$ cd redis
$ ./brief_run.sh pmdebugger 10000

Output: It directly output the result like

throughput (Get/Sec)
16355.3

Bug Detection Capability

We implement 9 rules in PMDebugger to detect bugs. To verify its capability of bug detection, we integrate those bug cases into Valgrind. Those bug cases are in valgrind-pmdebugger/pmdebugger/tests folder and classified by their bug type.

They are organized as the following structure:

• address_specific/ and logging_related/: Function test.
• no_durability_guarantee/: A persistent memory location, since the last write to it, is not persisted.
• multiple_overwrite/: The program writes to the same persistent memory location multiple times, before the durability of the memory location is guaranteed.
• no_order_guarantee/: The program cannot guarantee the order in which writes become persistent.
• redundant_flush/: A store to a memory location is flushed multiple times before the nearest fence.
• flush_nothing/: A CLF instruction does not persist any prior store.
• epoch_redundant_fence/: More than one fence can exist in an epoch section.
• lack_ordering_in_strand/: Persisting memory locations across strands can violate the order guarantee.
• epoch_durability/: At the end of an epoch, the durability of all memory locations updated by store instructions in the epoch cannot be guaranteed.
• redundant_logging/: In a logging-based transaction in PMDK, a data object is updated once but logged multiple times.

We can simply run the following command to verify bug detection capability:

$ cd <PMDebugger Root>/valgrind-pmdebugger
$ perl tests/vg_regtest pmdebugger

What to look at

After you run the command perl tests/vg_regtest pmdebugger, you can see something like the follow.

1 ...
2 -- Running  tests in pmdebugger/tests/redundant_flush ------------------
3 redundant_flush1: valgrind   --print-debug-detail=yes --isa-rec=no -q --flush-check=yes --flush-align=yes ./redundant_flush1 
4 redundant_flush2: valgrind   --print-debug-detail=yes -q --flush-check=yes --flush-align=yes ./redundant_flush2 
5 -- Finished tests in pmdebugger/tests/redundant_flush ------------------
6 ...
7 == 58 tests, 0 stderr failures, 0 stdout failures, 0 stderrB failures, 0 stdoutB failures, 0 post failures ==

In brief, You can directly look at the last row of the output (Line 7):

• 58 tests: showing a total number of bugs in this bug suite.
• stderr failures, stdout failures, stderrB failures, stdoutB and post failures: showing the number of detection failures in different cases. So debugging tool passes the examination if all these cases are 0.

For more detail, you can see Line 2 to Line 5.

• pmdebugger/tests/redundant_flush : showing bug type is redundant_flush. There are total nine bug types.
• redundant_flush1 and redundant_flush2: showing names of benchmark
• valgrind --print-debug-detail=yes -q --flush-check=yes --flush-align=yes ./redundant_flush1 : showing how to detect redundant_flush1 by PMDebugger.

In addition, there are total 10 snippets like above in the output (one for function tests and nine for validating bug detection rules)

New Bugs Found By PMDebugger

PMDebugger finds 19 new bugs in Memcached and two new bugs (confirmed by PMDK[1][2]) in PMDK. These bugs were not reported before. We provide scripts to reproduce these bugs.

• Bug 1, No durability guarantee in Memcached: Use the script memslap/new_bug1.sh to reproduce bugs.
• Bug 2, Redundant epoch fence in PMDK: Use the script pmdk/new_bug2.sh to reproduce the bug.
• Bug 3, Lack durability in epoch: Use the script pmdk/new_bug3.sh to reproduce the bug.

Experiment Customization

For workloads based on PMDK, you can directly debug their binary file with PMDebugger. For example:

$ valgrind --tool=pmdebugger ./WORKLOAD

For more details, please run valgrind --tool=pmdebugger -h.

For other workloads, you firstly require to insert annotations, such as epoch_begin (VALGRIND_PMC_EPOCH_BEGIN) and epoch_end (VALGRIND_PMC_EPOCH_END). Then, after recompiling workloads, you can debug them in the above way. Note that these annotations can be inserted into low_level API and achieve automatic annotation. For example, we insert epoch_begin and epoch_end in PMDK's TX_BEGIN and TX_END to achieve automatic annotation.