I wanted to make use of the much talked about feature in Gcc 4.5+ called Link Time Optimization in Pcompress. Link time optimizations promise to bring in advanced optimizations across compilation contexts. For example if you declare a function as “inline” in a source file and call it from other places in the same source file then all function calls will be replaced with the function body itself. However if you call the same function from another source file which is compiled separately then inlining will not happen across the files. Earlier Gcc support for this was called whole-program optimization but it was cumbersome and in some cases impossible to use. LTO enables a repeat optimization and re-compilation pass during linking of the final executable providing the ability to do such cross-file optimizations elegantly.
I am using Fedora 16 which has got Gcc 4.6.3 with support for LTO so it was a simple matter of tweaking the makefile. However, after adding the LTO flag something felt not right. The program felt a tad slower. When doing performance tweaks “feel” is the last thing you want to depend upon, so I added simple timing measurements to various modules within Pcompress. They measure the starting and ending monotonic wall clock times in milliseconds for processing a chunk of data and compute the throughput in terms of MBs per Second. What I saw after that was quite strange.
LTO appeared to actually reduce throughput performance by as much as 60%! My laptop had the following specs: Core i5 430M 2.27GHz, 8GB RAM. I was using Gcc 4.6.3 native build on Fedora. Suspecting something with the distro or the specific Gcc build I looked at another laptop I had. That was an AMD Piledriver 1.9 GHz with 4GB RAM running Linux Mint 13. It also had Gcc 4.6.3 but of course differen Ubuntu derived build. This also produced the same results. A 50% performance drop with LTO enabled. The outputs are below.
Output of “Normal” build without LTO
time ./pcompress -D -c lzmaMt -l14 -L -P -s110m w020n40.tar
Scaling to 2 threads
Original size: 86528000, blknum: 12726
Number of maxlen blocks: 116
Total Hashtable bucket collisions: 4477
Merge count: 12320
Deduped size: 85195878, blknum: 562, delta_calls: 0, delta_fails: 0
Dedupe speed 107.758 MB/s
LZP: Insize: 85193594, Outsize: 35087799
LZP: Processed at 178.023 MB/s
DELTA2: srclen: 35087799, dstlen: 35086691
DELTA2: header overhead: 192
DELTA2: Processed at 247.827 MB/s
Chunk compression speed 1.513 MB/s
real 0m24.058s
user 0m40.485s
sys 0m0.448s
Output of build with LTO enabled
time ./pcompress -D -c lzmaMt -l14 -L -P -s110m w020n40.tar
Scaling to 2 threads
Original size: 86528000, blknum: 12726
Number of maxlen blocks: 116
Total Hashtable bucket collisions: 4477
Merge count: 12320
Deduped size: 85195878, blknum: 562, delta_calls: 0, delta_fails: 0
Dedupe speed 32.146 MB/s
LZP: Insize: 85193594, Outsize: 35087799
LZP: Processed at 67.585 MB/s
DELTA2: srclen: 35087799, dstlen: 35086691
DELTA2: header overhead: 192
DELTA2: Processed at 84.044 MB/s
Chunk compression speed 0.828 MB/s
real 0m45.138s
user 1m17.835s
sys 0m0.612s
The pcompress invocations above uses LZMA compression in extreme mode (-l14) and also enables Deduplication, LZP and Delta2 encoding. The tarfile “w020n40.tar” is a Global Topographic Data (Digital Elevation Model) from USGS. I used that dataset since it contains a lot of embedded tables of repeating numeric data that Delta Encoding and LZP (as you can see above in this case) can detect and collapse.
The performance drop with LTO is drastic to say the least. To rule out any mistake in my throughput computation I also included the “time” command and the performance difference is clear in the time output as well. Similar results were visible on the AMD laptop running Linux Mint 13. In addition to LTO I was passing the following flags to the compiler:
-m64 -msse3 -c -O3 -ftree-vectorize -floop-interchange -floop-block
I then experimented with omitting those flags and reducing optimization level to -O2 but to no avail. I even tried “-fno-inline-functions” thinking excessive inlining might be causing cache overflows of loops. But that produced the same results. LTO kept on churning out lower performance numbers regardless of what I did. Subsequently I tried with Gcc 4.7.2. I built it from upstream sources and repeated my experiments with exactly the same results as before! Something was broken.
So my next step was to ask for help from the gem of an utility on Linux called simply as Perf. This can do a bunch of profiling on the system and apps and also provide CPU performance counter metrics. This tool is similar to some OpenSolaris tools like cpustat, intrstat etc. Perf has got a whole bunch of features but I looked at a couple of capabilities. I used “perf record” to collect profiling data from a pcompress run and used “perf stat -d” to dump detailed performance counter statistics. The outputs of perf stat were interesting but provided little clue as to the root cause:
Output of “perf stat -d” for normal non-LTO build
Performance counter stats for './pcompress -D -c lzmaMt -l14 -L -P -s110m w020n40.tar':
46724.903534 task-clock # 1.682 CPUs utilized
21,763 context-switches # 0.466 K/sec
1,800 CPU-migrations # 0.039 K/sec
49,634 page-faults # 0.001 M/sec
97,919,627,864 cycles # 2.096 GHz [40.01%]
60,976,690,063 stalled-cycles-frontend # 62.27% frontend cycles idle [39.99%]
42,558,445,705 stalled-cycles-backend # 43.46% backend cycles idle [39.97%]
83,132,473,549 instructions # 0.85 insns per cycle
# 0.73 stalled cycles per insn [49.93%]
10,757,966,536 branches # 230.241 M/sec [49.95%]
674,594,850 branch-misses # 6.27% of all branches [50.01%]
20,128,203,574 L1-dcache-loads # 430.781 M/sec [50.05%]
819,620,127 L1-dcache-load-misses # 4.07% of all L1-dcache hits [50.02%]
470,081,090 LLC-loads # 10.061 M/sec [40.06%]
291,066,964 LLC-load-misses # 61.92% of all LL-cache hits [39.99%]
27.781254809 seconds time elapsed
Output of “perf stat -d” for LTO enabled build
Performance counter stats for './pcompress -D -c lzmaMt -l14 -L -P -s110m w020n40.tar':
80593.375461 task-clock # 1.759 CPUs utilized
26,507 context-switches # 0.329 K/sec
889 CPU-migrations # 0.011 K/sec
2,696 page-faults # 0.033 K/sec
169,713,512,965 cycles # 2.106 GHz [40.02%]
116,359,748,762 stalled-cycles-frontend # 68.56% frontend cycles idle [40.02%]
44,479,101,832 stalled-cycles-backend # 26.21% backend cycles idle [40.04%]
147,677,082,105 instructions # 0.87 insns per cycle
# 0.79 stalled cycles per insn [50.04%]
12,176,677,580 branches # 151.088 M/sec [50.05%]
710,765,008 branch-misses # 5.84% of all branches [50.01%]
73,456,165,490 L1-dcache-loads # 911.442 M/sec [49.98%]
576,238,614 L1-dcache-load-misses # 0.78% of all L1-dcache hits [50.00%]
361,655,574 LLC-loads # 4.487 M/sec [39.94%]
236,828,595 LLC-load-misses # 65.48% of all LL-cache hits [39.97%]
45.811620815 seconds time elapsed
I have highlighted some of the differences of interest. As you can see LLC or Last Level Cache (L3 Cache) misses increased by 5% with LTO while L1 data cache hits actually increased. The frontend (the instruction fetch and decode primarily) had more stalled cycles with LTO. While a stall can be due to many reasons coupled with a higher LLC miss ratio it appears that more time was spent waiting to fetch data from memory.
Side Note: You may be wondering that the LLC cache miss ratio is very high even in the normal non-LTO case. Does that point to severe inefficiencies within Pcompress ? Not really since it is LZMA which is the culprit here. LZMA’s memory access pattern is cache-unfriendly. A cost one has to bear to get the almost unbeatable compression ratio. For comparison, I have included the perf stat output for LZ4 compression in the Appendix below. See that.
Next item on the agenda was to capture profile data via “perf record” and view it via “perf report”. Perf report typically lists functions or modules within an executable with the percentage of time they hogged during the execution. The list is sorted in descending order. I am reproducing the top 6 items from each of the profile data below.
Perf report snippet for normal non-LTO case
Samples: 172K of event 'cycles', Event count (approx.): 88229375574
50.31% pcompress pcompress [.] GetMatchesSpec1
32.22% pcompress pcompress [.] LzmaEnc_CodeOneBlock
4.02% pcompress pcompress [.] MixMatches3
2.61% pcompress pcompress [.] GetHeads4b
1.63% pcompress pcompress [.] dedupe_compress
1.45% pcompress pcompress [.] MatchFinderMt_GetMatches
Perf report snippet for LTO build
Samples: 326K of event 'cycles', Event count (approx.): 169052159163
39.39% pcompress pcompress [.] GetMatchesSpec1
29.70% pcompress pcompress [.] GetOptimum.4390
4.91% pcompress pcompress [.] LitEnc_GetPriceMatched.4145
3.22% pcompress pcompress [.] MixMatches3
2.87% pcompress pcompress [.] dedupe_compress
2.72% pcompress pcompress [.] GetHeads4b.5262
We can immediately notice a few differences. The LTO enabled build lists a few new symbols that are not present in the non-LTO case. These are actually functions within the LZMA implementation. The numeric suffix to the function names is an LTO artifact. Why are these functions not showing up in the non-LTO build? Lets check the different binaries using objdump.
Objdump output for normal non-LTO binary
objdump -t pcompress | grep GetOptimum
[pcompress]$
Objdump output for LTO enabled binary
objdump -t pcompress | grep GetOptimum
00000000004284c4 g F .text 00000000000004d5 .hidden GetOptimumFast.4368
000000000042a351 g F .text 0000000000002412 .hidden GetOptimum.4390
Objdump output for non-LTO DEBUG binary (No Optimizations)
objdump -t pcompress | grep GetOptimum
000000000040fd05 l F .text 0000000000002570 GetOptimum
0000000000412275 l F .text 0000000000000515 GetOptimumFast
Something jumps out as obvious now. The GetOptimum function is not visible in the optimized non-LTO build but is visible in the DEBUG build. This is a static function in LzmaEnc.c and is large but still gets inlined in the normal optimized build. LTO however is actually preventing some useful inlining from happening. I inspected the disassembly using “objdump -d” to verify that. In the LzmaEnc.c source file one of the call sequence is thus:
LzmaEnc_CodeOneMemBlock -> LzmaEnc_CodeOneBlock -> GetOptimum
In the normal non-LTO optimized build LzmaEnc_CodeOneMemBlock() is a single large function with the other functions above inlined and further optimized. In the LTO build the call sequence is thus:
LzmaEnc_CodeOneMemBlock ->LzmaEnc_CodeOneBlock.4411 ->
LzmaEnc_CodeOneBlock.part.8.4405 -> GetOptimum.4390
No wonder this causes a major performance drop since inlining in turn enables a series of other optimizations like Value Range Propagation, Code Motion etc which LTO is preventing by preventing inlining. So the lesson of the day is to use LTO with a spoonful of salt. Do not just use it and be happy. Actually benchmark and verify whether you are benefitting from it or not. We should see some improvement in this space with Gcc 4.8 (see below).
Appendix
Output of perf stat -d for LZ4 compression method
Performance counter stats for './pcompress -D -c lz4 -l14 -L -P -s110m w020n40.tar':
3943.008621 task-clock # 0.996 CPUs utilized
440 context-switches # 0.112 K/sec
9 CPU-migrations # 0.002 K/sec
2,179 page-faults # 0.553 K/sec
8,275,383,339 cycles # 2.099 GHz [39.97%]
3,510,592,770 stalled-cycles-frontend # 42.42% frontend cycles idle [39.91%]
2,314,266,052 stalled-cycles-backend # 27.97% backend cycles idle [39.93%]
10,907,435,475 instructions # 1.32 insns per cycle
# 0.32 stalled cycles per insn [49.95%]
1,754,196,387 branches # 444.888 M/sec [49.93%]
60,384,786 branch-misses # 3.44% of all branches [50.05%]
2,588,170,552 L1-dcache-loads # 656.395 M/sec [50.10%]
196,213,428 L1-dcache-load-misses # 7.58% of all L1-dcache hits [50.17%]
57,790,609 LLC-loads # 14.656 M/sec [40.14%]
2,870,453 LLC-load-misses # 4.97% of all LL-cache hits [40.11%]
3.957535121 seconds time elapsed
The interesting thing to note here is the LLC cache miss ratio. Due to it’s near linear access pattern LZ4 is making good use of the cache. This is one of the reasons why it is so fast. Of course LZ4 cannot even think of matching LZMA in terms of compression ratio but then, that is not it’s intent either. In addition it also shows that LZP and my Dedupe and Delta2 Encoding implementations are cache-efficient as well.
![A[m X n] -> A"[n X m]](http://s0.wp.com/latex.php?latex=A%5Bm+X+n%5D+-%3E+A%22%5Bn+X+m%5D&bg=ffffff&fg=444444&s=0 "A[m X n] -> A\"[n X m]")
where m and n are dimensions of the 2D matrices.![A[4X3] = \left| \begin{array}{cccc} 00 & 00 & 11 & D7 \\ 00 & 00 & 0D & 97 \\ 00 & 00 & 24 & 81 \end{array} \right|](http://s0.wp.com/latex.php?latex=A%5B4X3%5D+%3D+%5Cleft%7C+%5Cbegin%7Barray%7D%7Bcccc%7D+00+%26+00+%26+11+%26+D7+%5C%5C+00+%26+00+%26+0D+%26+97+%5C%5C+00+%26+00+%26+24+%26+81+%5Cend%7Barray%7D+%5Cright%7C&bg=ffffff&fg=444444&s=0 "A[4X3] = \left| \begin{array}{cccc} 00 & 00 & 11 & D7 \\ 00 & 00 & 0D & 97 \\ 00 & 00 & 24 & 81 \end{array} \right|")
.![A"[3X4] = \left| \begin{array}{ccc} 00 & 00 & 00 \\ 00 & 00 & 00 \\ 11 & 0D & 24 \\ D7 & 97 & 81 \end{array} \right|](http://s0.wp.com/latex.php?latex=A%22%5B3X4%5D+%3D+%5Cleft%7C+%5Cbegin%7Barray%7D%7Bccc%7D+00+%26+00+%26+00+%5C%5C+00+%26+00+%26+00+%5C%5C+11+%26+0D+%26+24+%5C%5C+D7+%26+97+%26+81+%5Cend%7Barray%7D+%5Cright%7C&bg=ffffff&fg=444444&s=0 "A\"[3X4] = \left| \begin{array}{ccc} 00 & 00 & 00 \\ 00 & 00 & 00 \\ 11 & 0D & 24 \\ D7 & 97 & 81 \end{array} \right|")
. If we store this in memory the byte sequence becomes: 00 00 00 00 00 00 11 0D 24 D7 97 81. Voila, all the zeroes have lined up. Any compression algorithm on the planet will now be able to collapse that sequence of zeroes. This example shows zeroes, but in fact, it can be any repeating byte value.
. However this approach looked suspect to me. Eventually after reading up more stuff it turns out that 
. The HMAC is then inserted into the Header. The HMAC portion of the header is zeroed when actually computing the HMAC. Since the HMAC is computed over the compressed data, it needs to process a smaller dataset and benefits performance.
