Scal 
Scal is an open-source benchmarking framework that provides (1) software infrastructure for executing concurrent data structure algorithms, (2) workloads for benchmarking their performance and scalability, and (3) implementations of a large set of concurrent data structures.
Homepage: http://scal.cs.uni-salzburg.at
Paper: Scal: A Benchmarking Suite for Concurrent Data Structures
Data structures
| Name | Semantics | Year | Ref |
|---|---|---|---|
| Lock-based Singly-linked List Queue | strict queue | 1968 | [1] |
| Michael Scott (MS) Queue | strict queue | 1996 | [2] |
| Flat Combining Queue | strict queue | 2010 | [3] |
| Wait-free Queue | strict queue | 2012 | [4] |
| Linked Cyclic Ring Queue (LCRQ) | strict queue | 2013 | [5] |
| Timestamped (TS) Queue | strict queue | 2015 | [6] |
| Cooperative TS Queue | strict queue | 2015 | [7] |
| Segment Queue | k-relaxed queue | 2010 | [8] |
| Random Dequeue (RD) Queue | k-relaxed queue | 2010 | [8] |
| Bounded Size k-FIFO Queue | k-relaxed queue, pool | 2013 | [9] |
| Unbounded Size k-FIFO Queue | k-relaxed queue, pool | 2013 | [9] |
| b-RR Distributed Queue (DQ) | k-relaxed queue, pool | 2013 | [10] |
| Least-Recently-Used (LRU) DQ | k-relaxed queue, pool | 2013 | [10] |
| Locally Linearizable DQ (static, dynamic) |
locally linearizable queue, pool | 2015 | [11] |
| Locally Linearizable k-FIFO Queue | locally linearizable queue k-relaxed queue, pool |
2015 | [11] |
| Relaxed TS Queue | quiescently consistent queue (conjectured) |
2015 | [7] |
| Lock-based Singly-linked List Stack | strict stack | 1968 | [1] |
| Treiber Stack | strict stack | 1986 | [12] |
| Elimination-backoff Stack | strict stack | 2004 | [13] |
| Timestamped (TS) Stack | strict stack | 2015 | [6] |
| k-Stack | k-relaxed stack | 2013 | [14] |
| b-RR Distributed Stack (DS) | k-relaxed stack, pool | 2013 | [10] |
| Least-Recently-Used (LRU) DS | k-relaxed stack, pool | 2013 | [10] |
| Locally Linearizable DS (static, dynamic) |
locally linearizable stack, pool | 2015 | [11] |
| Locally Linearizable k-Stack | locally linearizable stack k-relaxed queue, pool |
2015 | [11] |
| Timestamped (TS) Deque | strict deque (conjectured) | 2015 | [7] |
| d-RA DQ and DS | strict pool | 2013 | [10] |
Dependencies
On Ubuntu (β₯ 14.04) based systems:
sudo apt-get install --fix-missing google-perftools libgoogle-perftools-dev cmake libgtest-dev libgflags-dev
On Debian (jessie) based systems:
sudo apt-get install build-essential autoconf libtool google-perftools libgoogle-perftools-dev cmake libgtest-dev libgflags2 libgflags-dev
Building
Note: We switched from autotools to gyp for building the framework. The old files are still present in the checkout but will be removed once everything is converted.
This is as easy as
tools/make_deps.sh
build/gyp/gyp --depth=. scal.gyp
V=1 BUILDTYPE=Debug make
V=1 BUILDTYPE=Release make
The debug and release builds reside in out/.
Additional data files, such as graph files, are available as submodule
git submodule init
git submodule update data
The resulting files reside in data/.
Examples
Producer/consumer
The most common parameters are:
- consumers: Number of consuming threads
- producers: Number of producing threads
- c: The computational workload (iterative pi calculation) between two data structure operations
- operations: The number of put/enqueue operations the should be performed by a producer
The following runs the Michael-Scott queue in a producer/consumer benchmark:
./prodcon-ms -producers=15 -consumers=15 -operations=100000 -c=250
And the same for the bounded-size k-FIFO queue:
./prodcon-bs-kfifo -producers=15 -consumers=15 -operations=100000 -c=250
And for Distributed Queue with a 1-random balancer:
./prodcon-dds-1random-ms -producers=15 -consumers=15 -operations=100000 -c=250
Try ./prodcon-<data_structure> --help to see the full list of available parameters.
References
-
D. E. Knuth. The Art of Computer Programming, Volume 1 (3rd Ed.): Fundamental Algorithms. Addison Wesley, Redwood City, CA, USA, 1997.
-
M.M. Michael and M.L. Scott. Simple, fast, and practical non-blocking and blocking concurrent queue algorithms. In Proc. Symposium on Principles of Distributed Computing (PODC), pages 267β275. ACM, 1996.
-
D. Hendler, I. Incze, N. Shavit, and M. Tzafrir. Flat combining and the synchronization-parallelism tradeoff. In Proc. Symposium on Parallelism in Algorithms and Architectures (SPAA), pages 355β364. ACM, 2010.
-
A. Kogan and E. Petrank. A methodology for creating fast wait-free data structures. In Proc. Symposium on Principles and Practice of Parallel Programming (PPoPP), pages 141β150. ACM, 2012.
-
A. Morrison and Y. Afek. Fast concurrent queues for x86 processors. In Proc. Symposium on Principles and Practice of Parallel Programming (PPoPP), pages 103β112. ACM, 2013.
-
M. Dodds, A. Haas, and C.M. Kirsch. A scalable, correct time-stamped stack. In Proc. Symposium on Principles of Programming Languages (POPL), pages 233β 246. ACM, 2015.
-
A. Haas. Fast Concurrent Data Structures Through Timestamping. PhD thesis, University of Salzburg, Salzburg, Austria, 2015.
-
Y. Afek, G. Korland, and E. Yanovsky. Quasi-linearizability: Relaxed consistency for improved concurrency. In Proc. Conference on Principles of Distributed Systems (OPODIS), pages 395β410. Springer, 2010.
-
C.M. Kirsch, M. Lippautz, and H. Payer. Fast and scalable, lock-free k-fifo queues. In Proc. International Conference on Parallel Computing Technologies (PaCT), LNCS, pages 208β223. Springer, 2013.
-
A. Haas, T.A. Henzinger, C.M. Kirsch, M. Lippautz, H. Payer, A. Sezgin, and A. Sokolova. Distributed queues in shared memoryβmulticore performance and scalability through quantitative relaxation. In Proc. International Conference on Computing Frontiers (CF). ACM, 2013.
-
A. Haas, T.A. Henzinger, A.Holzer, C.M. Kirsch, M. Lippautz, H. Payer, A. Sezgin, A. Sokolova, and H. Veith. Local linearizability. CoRR, abs/1502.07118, 2015.
-
R.K. Treiber. Systems programming: Coping with parallelism. Technical Report RJ-5118, IBM Research Center, 1986.
-
D. Hendler, N. Shavit, and L. Yerushalmi. A scalable lock-free stack algorithm. In Proc. Symposium on Parallelism in Algorithms and Architectures (SPAA), pages 206β215. ACM, 2004.
-
T.A. Henzinger, C.M. Kirsch, H. Payer, A. Sezgin, and A. Sokolova. Quantitative relaxation of concurrent data structures. In Proc. Symposium on Principles of Programming Languages (POPL), pages 317β328. ACM, 2013.
License
Copyright (c) 2012-2016, the Scal Project Authors. All rights reserved. Please see the AUTHORS file for details.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those of the authors and should not be interpreted as representing official policies, either expressed or implied, of the Scal Project.