soedinglab / MMseqs2

MMseqs2: ultra fast and sensitive search and clustering suite
https://mmseqs.com
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AMD EPYC 7742 is worse than Intel Xeon Silver 4210? #441

Open Ting1215 opened 3 years ago

Ting1215 commented 3 years ago

I got a new computer, 2AMD EPYC 7742 64-Core Processor, Mem: 256G, Swap: 16G. But it is only half the speed of the old computer (2Intel Xeon Silver 4210, Mem:64G, Swap:2G). Please give me some suggestions, thank you very much.

milot-mirdita commented 3 years ago

Could you tell something about your input (and target database/search settings)?

We generally have had good experience with our own two socket Epic 7742 systems. One thing where the Epyc system is at a disadvantage is currently on IO. We write one output file per thread and these have to be merged in some steps. With 256 merges this can take quite a bit of time, in this case the Intel system would be slower in computing but as it has less threads it might be faster in merging and might thus win overall.

milot-mirdita commented 3 years ago

Do you notice that the system is swapping a lot? 1GB RAM per thread is also very little. Our systems usually have 8 to 16 GB RAM per thread.

Ting1215 commented 3 years ago

Could you tell something about your input (and target database/search settings)?

We generally have had good experience with our own two socket Epic 7742 systems. One thing where the Epyc system is at a disadvantage is currently on IO. We write one output file per thread and these have to be merged in some steps. With 256 merges this can take quite a bit of time, in this case the Intel system would be slower in computing but as it has less threads it might be faster in merging and might thus win overall.

Thank you very much for your reply. Because I just want to test the new computer, I just entered these. The input file is very small. "mmseqs taxonomy /data/testdb /data/datenbank/silvadb /data/testdb_result /data/tmp". For my old computer (2Intel Xeon Silver 4210, Mem: 64G, Swap: 2G) it only takes 4 minutes, but this new computer takes 10 minutes. I have tried adding the following commands "--threads 10; --threads 20; --threads 40; --threads 128; --threads 256". But it takes at least 10 minutes. When --threads is greater than 100, the computer can no longer perform any other operations. (Memory usage never exceeds 20G.) After reading some information, we have a few unsure questions. Does the memory frequency (2933 or 3200) affect the operation of the program? We are now using 8 pieces of memory. If you add 8 pieces, can the current problem be improved?

Ting1215 commented 3 years ago

Do you notice that the system is swapping a lot? 1GB RAM per thread is also very little. Our systems usually have 8 to 16 GB RAM per thread.

Thank you very much for your reply. Due to our current work, only 40G memory was used at the highest time. I believe your suggestions may be important to our next project. I didn’t find any changes in swapping. When I noticed it was always 0. During the test,memory usage never exceeds 20G. But the calculation speed is very slow. And when the thread is greater than 100, other operations cannot be performed.

milot-mirdita commented 3 years ago

Could you post the full log of the two runs? That would make it easier to say what might be going wrong.

Ting1215 commented 3 years ago

Could you post the full log of the two runs? That would make it easier to say what might be going wrong.

Thank you! runs of EPYC 7742 (base) ct@test:~$ ~/mmseqs/bin/mmseqs taxonomy /data/ct/test/testdb /data/ct/datenbank/silva_mmseqs/silva /data/ct/test/test1213 /data/ct/tmp taxonomy /data/ct/test/testdb /data/ct/datenbank/silva_mmseqs/silva /data/ct/test/test1213 /data/ct/tmp

MMseqs Version: b1704ccc17f3814a1d640a83c6aa925bb0406809 ORF filter 1 ORF filter e-value 100 ORF filter sensitivity 2 LCA mode 3 Taxonomy output mode 0 Majority threshold 0.5 Vote mode 1 LCA ranks
Column with taxonomic lineage 0 Compressed 0 Threads 256 Verbosity 3 Taxon blacklist 12908:unclassified sequences,28384:other sequences Substitution matrix nucl:nucleotide.out,aa:blosum62.out Add backtrace false Alignment mode 1 Alignment mode 0 Allow wrapped scoring false E-value threshold 1 Seq. id. threshold 0 Min alignment length 0 Seq. id. mode 0 Alternative alignments 0 Coverage threshold 0 Coverage mode 0 Max sequence length 65535 Compositional bias 1 Max reject 5 Max accept 30 Include identical seq. id. false Preload mode 0 Pseudo count a 1 Pseudo count b 1.5 Score bias 0 Realign hits false Realign score bias -0.2 Realign max seqs 2147483647 Gap open cost nucl:5,aa:11 Gap extension cost nucl:2,aa:1 Zdrop 40 Seed substitution matrix nucl:nucleotide.out,aa:VTML80.out Sensitivity 2 k-mer length 0 k-score 2147483647 Alphabet size nucl:5,aa:21 Max results per query 300 Split database 0 Split mode 2 Split memory limit 0 Diagonal scoring true Exact k-mer matching 0 Mask residues 1 Mask lower case residues 0 Minimum diagonal score 15 Spaced k-mers 1 Spaced k-mer pattern
Local temporary path
Rescore mode 0 Remove hits by seq. id. and coverage false Sort results 0 Mask profile 1 Profile E-value threshold 0.001 Global sequence weighting false Allow deletions false Filter MSA 1 Maximum seq. id. threshold 0.9 Minimum seq. id. 0 Minimum score per column -20 Minimum coverage 0 Select N most diverse seqs 1000 Min codons in orf 30 Max codons in length 32734 Max orf gaps 2147483647 Contig start mode 2 Contig end mode 2 Orf start mode 1 Forward frames 1,2,3 Reverse frames 1,2,3 Translation table 1 Translate orf 0 Use all table starts false Offset of numeric ids 0 Create lookup 0 Add orf stop false Overlap between sequences 0 Sequence split mode 1 Header split mode 0 Chain overlapping alignments 0 Merge query 1 Search type 0 Search iterations 1 Start sensitivity 4 Search steps 1 Exhaustive search mode false Filter results during exhaustive search 0 Strand selection 1 LCA search mode false Disk space limit 0 MPI runner
Force restart with latest tmp false Remove temporary files false

Accel. 2bLCA cannot be used with nucl-nucl taxonomy, using top-hit instead search /data/ct/test/testdb /data/ct/datenbank/silva_mmseqs/silva /data/ct/tmp/17728638439372299279/first /data/ct/tmp/17728638439372299279/tmp_hsp1 --alignment-mode 1 -e 1 --max-rejected 5 --max-accept 30 -s 2 --spaced-kmer-mode 1 --min-length 30 --max-length 32734 --orf-start-mode 1

prefilter /data/ct/tmp/17728638439372299279/tmp_hsp1/7295210961924303337/query_seqs_split /data/ct/datenbank/silva_mmseqs/silva.idx /data/ct/tmp/17728638439372299279/tmp_hsp1/7295210961924303337/search/pref_0 --sub-mat nucl:nucleotide.out,aa:blosum62.out --seed-sub-mat nucl:nucleotide.out,aa:VTML80.out -k 15 --k-score 2147483647 --alph-size nucl:5,aa:21 --max-seq-len 10000 --max-seqs 300 --split 0 --split-mode 2 --split-memory-limit 0 -c 0 --cov-mode 0 --comp-bias-corr 1 --diag-score 1 --exact-kmer-matching 1 --mask 1 --mask-lower-case 0 --min-ungapped-score 15 --add-self-matches 0 --spaced-kmer-mode 1 --db-load-mode 0 --pca 1 --pcb 1.5 --threads 256 --compressed 0 -v 3 -s 2.0

Index version: 16 Generated by: b1704ccc17f3814a1d640a83c6aa925bb0406809 ScoreMatrix: nucleotide.out Query database size: 8000 type: Nucleotide Estimated memory consumption: 18G Target database size: 510984 type: Nucleotide Process prefiltering step 1 of 1

k-mer similarity threshold: 0 Starting prefiltering scores calculation (step 1 of 1) Query db start 1 to 8000 Target db start 1 to 510984 ^C===============> ] 25.00% 2.00K eta 5m 1s

Runs of Intel computer [kabru@bwp-cfdmain test]$ mmseqs taxonomy testdb ~/datenbank/silvadb testx_result tmp taxonomy testdb /home/kabru/datenbank/silvadb testx_result tmp

MMseqs Version: b1704ccc17f3814a1d640a83c6aa925bb0406809 ORF filter 1 ORF filter e-value 100 ORF filter sensitivity 2 LCA mode 3 Taxonomy output mode 0 Majority threshold 0.5 Vote mode 1 LCA ranks
Column with taxonomic lineage 0 Compressed 0 Threads 72 Verbosity 3 Taxon blacklist 12908:unclassified sequences,28384:other sequences Substitution matrix nucl:nucleotide.out,aa:blosum62.out Add backtrace false Alignment mode 1 Alignment mode 0 Allow wrapped scoring false E-value threshold 1 Seq. id. threshold 0 Min alignment length 0 Seq. id. mode 0 Alternative alignments 0 Coverage threshold 0 Coverage mode 0 Max sequence length 65535 Compositional bias 1 Max reject 5 Max accept 30 Include identical seq. id. false Preload mode 0 Pseudo count a 1 Pseudo count b 1.5 Score bias 0 Realign hits false Realign score bias -0.2 Realign max seqs 2147483647 Gap open cost nucl:5,aa:11 Gap extension cost nucl:2,aa:1 Zdrop 40 Seed substitution matrix nucl:nucleotide.out,aa:VTML80.out Sensitivity 2 k-mer length 0 k-score 2147483647 Alphabet size nucl:5,aa:21 Max results per query 300 Split database 0 Split mode 2 Split memory limit 0 Diagonal scoring true Exact k-mer matching 0 Mask residues 1 Mask lower case residues 0 Minimum diagonal score 15 Spaced k-mers 1 Spaced k-mer pattern
Local temporary path
Rescore mode 0 Remove hits by seq. id. and coverage false Sort results 0 Mask profile 1 Profile E-value threshold 0.001 Global sequence weighting false Allow deletions false Filter MSA 1 Maximum seq. id. threshold 0.9 Minimum seq. id. 0 Minimum score per column -20 Minimum coverage 0 Select N most diverse seqs 1000 Min codons in orf 30 Max codons in length 32734 Max orf gaps 2147483647 Contig start mode 2 Contig end mode 2 Orf start mode 1 Forward frames 1,2,3 Reverse frames 1,2,3 Translation table 1 Translate orf 0 Use all table starts false Offset of numeric ids 0 Create lookup 0 Add orf stop false Overlap between sequences 0 Sequence split mode 1 Header split mode 0 Chain overlapping alignments 0 Merge query 1 Search type 0 Search iterations 1 Start sensitivity 4 Search steps 1 Exhaustive search mode false Filter results during exhaustive search 0 Strand selection 1 LCA search mode false Disk space limit 0 MPI runner
Force restart with latest tmp false Remove temporary files false

Accel. 2bLCA cannot be used with nucl-nucl taxonomy, using top-hit instead Create directory tmp/14943602774421092333/tmp_hsp1 search testdb /home/kabru/datenbank/silvadb tmp/14943602774421092333/first tmp/14943602774421092333/tmp_hsp1 --alignment-mode 1 -e 1 --max-rejected 5 --max-accept 30 -s 2 --spaced-kmer-mode 1 --min-length 30 --max-length 32734 --orf-start-mode 1

extractframes testdb tmp/14943602774421092333/tmp_hsp1/7440312624435503015/query_seqs --forward-frames 1 --reverse-frames 1 --create-lookup 0 --threads 72 --compressed 0 -v 3

[> ] 1.35% 55 e[> ] 1.28% 52 e[> ] 1.10% 45 e[> ] 1.08% 44 e[> ] 1.23% 50 e[=> ] 1.55% 63 e[> ] 1.03% 42 e[> ] 1.13% 46 e[=> ] 1.80% 73 e[=> ] 1.88% 76 e[=> ] 2.80% 113 [=> ] 2.83% 114 [> ] 1.15% 47 e[> ] 1.38% 56 e[=> ] 3.05% 123 [=> ] 3.08% 124 [> ] 1.48% 60 e[=> ] 1.60% 65 e[==> ] 3.28% 132 [=> ] 1.78% 72 e[=> ] 1.58% 64 e[==> ] 3.50% 141 [==> ] 3.55% 143 [==> ] 3.65% 147 [==> ] 3.70% 149 [==> ] 3.83% 154 [==> ] 3.88% 156 [=> ] 1.68% 68 e[> ] 1.50% 61 e[==> ] 4.08% 164 [==> ] 4.10% 165 [=> ] 1.95% 79 e[> ] 1.33% 54 e[==> ] 4.33% 174 [=> ] 1.98% 80 e[==> ] 4.50% 181 [==> ] 4.55% 183 [==> ] 4.53% 182 [=> ] 1.65% 67 e[=> ] 2.33% 94 e[=> ] 2.23% 90 e[===> ] 4.78% 192 [===> ] 4.80% 193 [=> ] 2.45% 99 e[=> ] 2.50% 101 [=> ] 2.40% 97 e[=> ] 2.60% 105 [=> ] 2.58% 104 [===> ] 4.95% 199 [=> ] 2.65% 107 [===> ] 5.00% 201 [=> ] 2.70% 109 [===> ] 5.15% 207 [===> ] 5.18% 208 [===> ] 5.20% 209 [=> ] 2.68% 108 [=> ] 1.63% 66 e[===> ] 5.55% 223 [> ] 1.43% 58 e[=> ] 2.90% 117 [===> ] 5.73% 230 [===> 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35.38% 1.4[=====================> ] 32.98% 1.3[====================> ] 31.06% 1.2[=======================> ] 35.48% 1.4[=======================> ] 35.51% 1.4[=======================> ] 35.61% 1.4[=======================> ] 35.63% 1.4[=================> ] 26.46% 1.0[=====================> ] 33.23% 1.3[=====================> ] 33.41% 1.3[=======================> ] 35.93% 1.4[=======================> ] 35.96% 1.4[=====================> ] 33.66% 1.3[=====================> ] 33.68% 1.3[=======================> ] 36.21% 1.4[=====================> ] 33.73% 1.3[=======================> ] 36.28% 1.4[=======================> ] 36.31% 1.4[=====================> ] 33.76% 1.3[====================> ] 31.83% 1.2[=======================> ] 36.56% 1.4[=======================> ] 36.61% 1.4[=======================> ] 36.63% 1.4[=======================> ] 36.76% 1.4[=======================> ] 36.78% 1.4[====================> ] 31.93% 1.2[=======================> ] 36.86% 1.4[=======================> ] 36.91% 1.4[======================> ] 34.41% 1.3[=============> ] 20.86% 835[========================> ] 37.03% 1.4[======================> ] 34.71% 1.3[=====================> ] 32.41% 1.3[======================> ] 34.96% 1.4[========================> ] 37.23% 1.4[======================> ] 34.93% 1.4[=====================> ] 32.66% 1.3[========================> ] 37.31% 1.4[======================> ] 35.11% 1.4[========================> ] 37.43% 1.5[========================> ] 37.48% 1.5[==================> ] 28.58% 1.1[========================> ] 37.68% 1.5[========================> ] 37.73% 1.5[===============> ] 23.71% 949[=====================> ] 33.01% 1.3[========================> ] 38.03% 1.5[=======================> ] 35.46% 1.4[========================> ] 38.06% 1.5[=====================> ] 33.08% 1.3[========================> ] 38.21% 1.5[========================> ] 38.23% 1.5[=======================> ] 35.83% 1.4[=======================> ] 35.91% 1.4[=========================> ] 38.48% 1.5[========================> ] 38.46% 1.5[=========================> ] 38.51% 1.5[=========================> ] 38.53% 1.5[=======================> ] 36.23% 1.4[=========================> ] 38.68% 1.5[=========================> ] 38.71% 1.5[=======================> ] 36.51% 1.4[=======================> ] 36.53% 1.4[======================> ] 33.91% 1.3[=========================> ] 38.91% 1.5[=========================> ] 38.96% 1.5[=========================> ] 39.01% 1.5[=============> ] 20.51% 821[======================> ] 34.31% 1.3[=========================> ] 39.26% 1.5[=====================> ] 32.31% 1.2[===================> ] 30.11% 1.2[=========================> ] 39.36% 1.5[========================> ] 37.06% 1.4[=========================> ] 39.46% 1.5[========================> ] 37.18% 1.4[========================> ] 37.21% 1.4[=========================> ] 39.58% 1.5[=========================> ] 39.63% 1.5[=========================> ] 39.78% 1.5[=========================> ] 39.81% 1.5[========================> ] 37.33% 1.4[========================> ] 37.46% 1.5[======================> ] 35.33% 1.4[=========================> ] 39.98% 1.6[====================> ] 30.81% 1.2[==========================> ] 40.04% 1.6[==========================> ] 40.09% 1.6[=====================> ] 33.03% 1.3[==========================> ] 40.11% 1.6[========================> ] 38.16% 1.5[==========================> ] 40.49% 1.6[=======================> ] 35.66% 1.4[==================> ] 29.03% 1.1[==========================> ] 40.64% 1.6[==========================> ] 40.66% 1.6[=====================> ] 33.46% 1.3[==========================> ] 40.76% 1.6[==========================> ] 40.81% 1.6[=====================> ] 33.63% 1.3[=====================> ] 33.71% 1.3[==========================> ] 40.89% 1.6[==========================> ] 40.91% 1.6[=======================> ] 36.48% 1.4[==========================> ] 41.09% 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1.8[=================> ] 26.81% 1.0[============================> ] 43.09% 1.7[============================> ] 43.11% 1.7[===================> ] 29.36% 1.1[=============================> ] 45.61% 1.8[=========================> ] 38.86% 1.5[=============================> ] 45.71% 1.8[======================> ] 33.93% 1.3[============================> ] 43.64% 1.7[=================> ] 27.56% 1.1[=============================> ] 45.81% 1.8[=============================> ] 45.86% 1.8[=============================> ] 45.89% 1.8[============================> ] 43.79% 1.7[============================> ] 43.81% 1.7[=============================> ] 46.11% 1.8[=============================> ] 46.14% 1.8[==============================> ] 46.16% 1.8[============================> ] 44.04% 1.7[==============================> ] 46.29% 1.8[============================> ] 44.19% 1.7[==============================> ] 46.34% 1.8[==============================> ] 46.44% 1.8[======================> ] 35.26% 1.4[==============================> ] 46.51% 1.8[===========================> ] 42.49% 1.7[==============================> ] 46.59% 1.8[=============================> ] 44.69% 1.7[==============================> ] 46.71% 1.8[===========================> ] 42.86% 1.7[==============================> ] 46.84% 1.8[===========================> ] 42.91% 1.7[=============================> ] 45.26% 1.8[===========================> ] 42.96% 1.7[=============================> ] 45.41% 1.8[=======================> ] 35.88% 1.4[==============================> ] 47.04% 1.8[=============================> ] 45.49% 1.8[=============================> ] 45.51% 1.8[=============================> ] 45.54% 1.8[==============================> ] 47.11% 1.8[=============================> ] 45.56% 1.8[==============================> ] 47.21% 1.8[=============================> ] 45.59% 1.8[=============================> ] 45.64% 1.8[==============================> ] 47.31% 1.8[=============================> ] 45.74% 1.8[=========================> ] 38.98% 1.5[==============================> ] 47.46% 1.9[======================> ] 34.28% 1.3[=============================> ] 45.84% 1.8[==============================> ] 47.56% 1.9[============================> ] 43.71% 1.7[==============================> ] 47.66% 1.9[===============================> ] 47.76% 1.9[===============================> ] 47.81% 1.9[==========================> ] 41.51% 1.6[===========================> ] 41.59% 1.6[===============================> ] 48.04% 1.9[============================> ] 44.06% 1.7[============================> ] 44.21% 1.7[===============================> ] 48.19% 1.9[==============================> ] 46.46% 1.8[===============================> ] 48.26% 1.9[==============================> ] 46.49% 1.8[===============================> ] 48.36% 1.9[===========================> ] 42.51% 1.7[==============================> ] 46.64% 1.8[===========================> ] 42.84% 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1.9[===============================> ] 47.74% 1.9[===============================> ] 47.79% 1.9[==========================> ] 41.54% 1.6[===============================> ] 48.09% 1.9[================================> ] 49.81% 1.9[===============================> ] 48.24% 1.9[================================> ] 49.86% 2.0[================================> ] 49.91% 2.0[================================> ] 49.94% 2.0[================================> ] 49.96% 2.0[===============================> ] 48.39% 1.9[================================> ] 50.11% 2.0[==============================> ] 46.61% 1.8[================================> ] 50.24% 2.0[================================> ] 50.26% 2.0[==============================> ] 46.74% 1.8[========================> ] 38.18% 1.5[================================> ] 50.44% 2.0[==============================> ] 46.91% 1.8[================================> ] 50.49% 2.0[==============================> ] 46.96% 1.8[===============================> ] 48.89% 1.9[================================> ] 50.61% 2.0[===============================> ] 49.04% 1.9[==============================> ] 47.01% 1.8[================================> ] 50.74% 2.0[===========================> ] 43.04% 1.7[================================> ] 49.24% 1.9[=================================> ] 50.89% 2.0[==============================> ] 47.14% 1.8[================================> ] 49.31% 1.9[=================================> ] 50.99% 2.0[================================> ] 49.41% 1.9[=================================> ] 51.06% 2.0[=================================> ] 51.16% 2.0[=================================> ] 51.21% 2.0[============================> ] 43.66% 1.7[=========================> ] 39.28% 1.5[=================================> ] 51.39% 2.0[=================================> ] 51.44% 2.0[================================> ] 49.76% 1.9[=================================> ] 51.61% 2.0[================================> ] 49.79% 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58.01% 2.3[====================================> ] 56.11% 2.2[====================================> ] 56.14% 2.2[=====================================> ] 58.19% 2.3[================================> ] 50.16% 2.0[=====================================> ] 58.26% 2.3[=====================================> ] 58.29% 2.3[==============================> ] 46.89% 1.8[===============================> ] 48.69% 1.9[=====================================> ] 58.39% 2.3[=====================================> ] 58.41% 2.3[=====================================> ] 56.99% 2.2[=====================================> ] 58.44% 2.3[=====================================> ] 57.01% 2.2[======================================> ] 58.54% 2.3[===================================> ] 54.44% 2.1[======================================> ] 58.66% 2.3[===================================> ] 54.54% 2.1[=====================================> ] 57.29% 2.2[======================================> ] 58.79% 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2.1[========================================> ] 61.62% 2.4[=========================================> ] 63.67% 2.5[========================================> ] 61.67% 2.4[=========================================> ] 63.74% 2.5[========================================> ] 61.77% 2.4[=========================================> ] 63.79% 2.5[=======================================> ] 60.44% 2.4[=========================================> ] 63.84% 2.5[==============================> ] 46.94% 1.8[=========================================> ] 63.97% 2.5[=========================================> ] 63.99% 2.5[=====================================> ] 57.06% 2.2[=====================================> ] 57.26% 2.2[=========================================> ] 64.24% 2.5[=========================================> ] 64.34% 2.5[=========================================> ] 64.37% 2.5[========================================> ] 62.52% 2.5[========================================> ] 62.57% 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2.6[=========================================> ] 63.69% 2.5[==========================================> ] 65.84% 2.6[==========================================> ] 65.89% 2.6[=========================================> ] 63.82% 2.5[==========================================> ] 65.99% 2.6[=======================================> ] 60.42% 2.4[=======================================> ] 60.49% 2.4[==========================================> ] 66.12% 2.6[========================================> ] 62.02% 2.4[=======================================> ] 60.67% 2.4[===========================================> ] 66.22% 2.6[=========================================> ] 64.27% 2.5[========================================> ] 62.24% 2.4[=====================================> ] 57.31% 2.2[==========================================> ] 64.62% 2.5[===========================================> ] 66.37% 2.6[=======================================> ] 60.82% 2.4[===========================================> ] 66.44% 2.6[========================================> ] 62.79% 2.5[==========================================> ] 64.94% 2.6[===========================================> ] 66.52% 2.6[===========================================> ] 66.54% 2.6[==========================================> ] 65.12% 2.6[===========================================> ] 66.74% 2.6[====================================> ] 55.69% 2.2[===========================================> ] 66.77% 2.6[=========================================> ] 63.24% 2.5[===========================================> ] 66.87% 2.6[======================================> ] 59.81% 2.3[===========================================> ] 66.89% 2.6[=========================================> ] 63.47% 2.5[===========================================> ] 66.99% 2.6[=========================================> ] 63.59% 2.5[===========================================> ] 67.09% 2.6[========================================> ] 61.59% 2.4[===========================================> ] 67.19% 2.6[==========================================> ] 65.87% 2.6[===========================================> ] 67.22% 2.6[===========================================> ] 67.32% 2.6[===========================================> ] 67.42% 2.7[==========================================> ] 66.14% 2.6[===========================================> ] 67.47% 2.7[===========================================> ] 67.54% 2.7[===========================================> ] 66.24% 2.6[=========================================> ] 64.29% 2.5[============================================> ] 67.79% 2.7[============================================> ] 67.82% 2.7[============================================> ] 67.84% 2.7[============================================> ] 68.02% 2.7[============================================> ] 68.07% 2.7[============================================> ] 68.09% 2.7[=======================================> ] 60.94% 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2.7[=============================================> ] 69.62% 2.7[======================================> ] 59.34% 2.3[========================================> ] 62.92% 2.5[=============================================> ] 69.74% 2.7[=============================================> ] 69.77% 2.7[=======================================> ] 61.27% 2.4[=============================================> ] 69.97% 2.8[==========================================> ] 65.14% 2.6[=============================================> ] 70.04% 2.8[==========================================> ] 65.29% 2.6[=============================================> ] 70.09% 2.8[============================================> ] 68.67% 2.7[============================================> ] 68.74% 2.7[=============================================> ] 70.17% 2.8[=============================================> ] 70.19% 2.8[=============================================> ] 70.22% 2.8[=============================================> ] 70.32% 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3.4[===================================================> ] 78.97% 3.1[=======================================================> ] 85.82% 3.4[=======================================================> ] 85.87% 3.4[=======================================================> ] 85.07% 3.4[=======================================================> ] 86.07% 3.4[=======================================================> ] 86.15% 3.4[=====================================================> ] 82.80% 3.3[=======================================================> ] 85.22% 3.4[========================================================> ] 86.22% 3.4[=====================================================> ] 81.65% 3.2[=======================================================> ] 85.45% 3.4[========================================================> ] 86.32% 3.4[=======================================================> ] 85.47% 3.4[=======================================================> ] 85.50% 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3.8[===============================================================> ] 97.02% 3.8[===============================================================> ] 97.25% 3.8[===============================================================> ] 97.27% 3.8[===============================================================> ] 97.22% 3.8[===============================================================> ] 97.30% 3.8[===============================================================> ] 98.30% 3.9[===============================================================> ] 98.35% 3.9[===============================================================> ] 98.32% 3.9[==============================================================> ] 95.82% 3.8[================================================================>] 98.82% 3.9[================================================================>] 99.52% 3.9[================================================================>] 99.90% 4.0[=================================================================] 100.00% 4.00K 0s 55ms Time for merging to query_seqs_h: 0h 0m 0s 10ms Time for merging to query_seqs: 0h 0m 0s 19ms Time for processing: 0h 0m 0s 164ms splitsequence tmp/14943602774421092333/tmp_hsp1/7440312624435503015/query_seqs tmp/14943602774421092333/tmp_hsp1/7440312624435503015/query_seqs_split --max-seq-len 10000 --sequence-overlap 0 --sequence-split-mode 1 --headers-split-mode 0 --create-lookup 0 --threads 72 --compressed 0 -v 3

Time for processing: 0h 0m 0s 1ms prefilter tmp/14943602774421092333/tmp_hsp1/7440312624435503015/query_seqs_split /home/kabru/datenbank/silvadb.idx tmp/14943602774421092333/tmp_hsp1/7440312624435503015/search/pref_0 --sub-mat nucl:nucleotide.out,aa:blosum62.out --seed-sub-mat nucl:nucleotide.out,aa:VTML80.out -k 15 --k-score 2147483647 --alph-size nucl:5,aa:21 --max-seq-len 10000 --max-seqs 300 --split 0 --split-mode 2 --split-memory-limit 0 -c 0 --cov-mode 0 --comp-bias-corr 1 --diag-score 1 --exact-kmer-matching 1 --mask 1 --mask-lower-case 0 --min-ungapped-score 15 --add-self-matches 0 --spaced-kmer-mode 1 --db-load-mode 0 --pca 1 --pcb 1.5 --threads 72 --compressed 0 -v 3 -s 2.0

Index version: 16 Generated by: b1704ccc17f3814a1d640a83c6aa925bb0406809 ScoreMatrix: nucleotide.out Query database size: 8000 type: Nucleotide Estimated memory consumption: 14G Target database size: 510984 type: Nucleotide Process prefiltering step 1 of 1

k-mer similarity threshold: 0 Starting prefiltering scores calculation (step 1 of 1) Query db start 1 to 8000 Target db start 1 to 510984 [> ] 0.00% 1 et[> ] 1.00% 81 e[> ] 1.03% 83 e[> ] 1.01% 82 e[> ] 1.04% 84 e[> ] 1.05% 85 e[=> ] 2.05% 165 [=> ] 3.05% 245 [==> ] 4.05% 325 [===> ] 5.05% 405 [===> ] 6.05% 485 [====> ] 7.05% 565 [=====> ] 8.05% 645 [=====> ] 9.05% 725 [======> ] 10.05% 805[=======> ] 11.05% 885[=======> ] 12.05% 965[========> ] 13.05% 1.0[=========> ] 14.05% 1.1[=========> ] 15.05% 1.2[==========> ] 16.05% 1.2[===========> ] 17.05% 1.3[===========> ] 18.05% 1.4[============> ] 19.05% 1.5[=============> ] 20.05% 1.6[=============> ] 21.05% 1.6[==============> ] 22.05% 1.7[==============> ] 23.05% 1.8[===============> ] 24.05% 1.9[================> ] 25.05% 2.0[================> ] 26.05% 2.0^C eta 1m 38s

milot-mirdita commented 3 years ago

The output is kind of mangled. Could you execute both runs with empty tmp directories and redirect the whole output to a file please? You might also want to set the TTY=0 environment variable to disable the progress bar (not sure why it broke so badly in your output. See: https://github.com/soedinglab/MMseqs2/wiki#environment-variables-used-by-mmseqs2

Ting1215 commented 3 years ago

The output is kind of mangled. Could you execute both runs with empty tmp directories and redirect the whole output to a file please? You might also want to set the TTY=0 environment variable to disable the progress bar (not sure why it broke so badly in your output. See: https://github.com/soedinglab/MMseqs2/wiki#environment-variables-used-by-mmseqs2

Thank you very much! Thank you for your help. Our problem has been resolved. The order of memory insertion has been changed. Dankschön!

milot-mirdita commented 3 years ago

Bitteschön :) Was the RAM inserted only at the bank belong to a single socket? (So all memory access of socket 2 went through socket 1 or vise versa?)

Ting1215 commented 3 years ago

Bitteschön :) Was the RAM inserted only at the bank belong to a single socket? (So all memory access of socket 2 went through socket 1 or vise versa?)

When we put all 8 RAMs next to CPU0 or CPU1, the speed has been improved very well.

salvoc81 commented 3 years ago

Hello @milot-mirdita and @Ting1215

I am writing in this thread mainly because I am having a strikingly similar problem. Basically our EPYC 7742 machines are slower than Xeon E5-4627 CPUs.

@milot-mirdita following what you mentioned about the IO, I have noticed, with big surprise that the execution time is longer when using inxeded DBs. Note that I only use small proteomes (1000~20000 sequences). Additionally I am using:

Following are some info the present system: CPU: 2x 64-Core AMD EPYC 7742

Memory: RAM: total: 1.96 TiB used: 14.31 GiB (0.7%) Array-1: capacity: 2.05 TiB note: est. slots: 16 EC: Multi-bit ECC Device-1: P1-DIMMA1 size: 128 GiB speed: 2667 MT/s Device-2: P1-DIMMB1 size: 128 GiB speed: 2667 MT/s Device-3: P1-DIMMC1 size: 128 GiB speed: 2667 MT/s Device-4: P1-DIMMD1 size: 128 GiB speed: 2667 MT/s Device-5: P1-DIMME1 size: 128 GiB speed: 2667 MT/s Device-6: P1-DIMMF1 size: 128 GiB speed: 2667 MT/s Device-7: P1-DIMMG1 size: 128 GiB speed: 2667 MT/s Device-8: P1-DIMMH1 size: 128 GiB speed: 2667 MT/s Device-9: P2-DIMMA1 size: 128 GiB speed: 2667 MT/s Device-10: P2-DIMMB1 size: 128 GiB speed: 2667 MT/s Device-11: P2-DIMMC1 size: 128 GiB speed: 2667 MT/s Device-12: P2-DIMMD1 size: 128 GiB speed: 2667 MT/s Device-13: P2-DIMME1 size: 128 GiB speed: 2667 MT/s Device-14: P2-DIMMF1 size: 128 GiB speed: 2667 MT/s Device-15: P2-DIMMG1 size: 128 GiB speed: 2667 MT/s Device-16: P2-DIMMH1 size: 128 GiB speed: 2667 MT/s

Although the servers are in another building from the above output it seems there is no empty memory slot.

Thanks a lot guys,

I might open a separate issue if you find it more appropriate.

milot-mirdita commented 3 years ago

Hi @salvoc81, could you post a full log of both runs (on both AMD and Intel)? I am not sure how to tackle this.

salvoc81 commented 3 years ago

Hello @milot-mirdita and sorry for my late reply. It seems MMseqs gets slower when using preindexed databases on AMD, although it was unexpected. I will try to post the logs in a few week, ad the Intel machine is being completely used by other colleagues.

milot-mirdita commented 3 years ago

Thank you, I can try to reproduce that once I am back from holidays. I don't think we tried preindexed databases on our epyc machines yet

salvoc81 commented 3 years ago

Hello Milot. Sorry for my awfully late reply.
I did some extra check and I discovered that the SSD on the server with EPYC cpus is slower (TLC, SATA) than that on the server with Xeon (MLC, PCIe). I would love to know suggested to add such store on 128 CPUs server for computing :)

Given the above I think the issuse regarding the speed VS Intel is solved.
Nevertheless, do you think that would be enough to justify MMseqs (and Diamond) being slower with preindexed DB? If not, and if you can find the same issue, maybe you should consider closing this one, and opening a separate issue.