1593 lines
34 KiB
HTML
1593 lines
34 KiB
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obikmer
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NUMA-aware worker pools
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obikmer
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Theory
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Theory
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<ul class="md-nav__list" data-md-scrollfix>
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Kmers and super-kmers
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</span>
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</a>
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</li>
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<span class="md-ellipsis">
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DNA encoding
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</span>
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</a>
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</li>
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<span class="md-ellipsis">
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Entropy filter
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</span>
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</a>
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</li>
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<span class="md-ellipsis">
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Minimizer selection
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</span>
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</a>
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</li>
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<span class="md-ellipsis">
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Partitioning architecture
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</span>
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</a>
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</li>
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<span class="md-ellipsis">
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Central-position SNP distance (discussion)
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</span>
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</a>
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</ul>
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</nav>
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Implementation
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Implementation
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</label>
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<ul class="md-nav__list" data-md-scrollfix>
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<a href="../../implementation/superkmer/" class="md-nav__link">
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<span class="md-ellipsis">
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SuperKmer
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</span>
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../../implementation/kmer/" class="md-nav__link">
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<span class="md-ellipsis">
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Kmer
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</span>
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../../implementation/chunkreader/" class="md-nav__link">
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<span class="md-ellipsis">
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Chunk reader
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</span>
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../../implementation/pipeline/" class="md-nav__link">
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<span class="md-ellipsis">
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Construction pipeline
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||
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</span>
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../../implementation/obipipeline/" class="md-nav__link">
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||
|
||
|
||
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<span class="md-ellipsis">
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||
|
||
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||
obipipeline library
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||
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||
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</span>
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||
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||
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||
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../../implementation/storage/" class="md-nav__link">
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||
|
||
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||
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||
<span class="md-ellipsis">
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||
|
||
|
||
On-disk storage
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
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||
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||
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||
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||
|
||
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||
|
||
|
||
|
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||
<li class="md-nav__item">
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<a href="../../implementation/mphf/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
MPHF selection
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
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||
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||
|
||
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|
||
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|
||
<li class="md-nav__item">
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||
<a href="../../implementation/unitig_evidence/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Unitig evidence encoding
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
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||
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|
||
|
||
|
||
|
||
|
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|
||
<li class="md-nav__item">
|
||
<a href="../../implementation/evidence_elimination/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Evidence elimination (discussion)
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
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|
||
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|
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|
||
|
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|
||
<li class="md-nav__item">
|
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<a href="../../implementation/obilayeredmap/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
obilayeredmap crate
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
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|
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||
|
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|
||
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|
||
<li class="md-nav__item">
|
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<a href="../../implementation/persistent_compact_int_vec/" class="md-nav__link">
|
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|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
PersistentCompactIntVec
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
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</li>
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<li class="md-nav__item">
|
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<a href="../../implementation/persistent_bit_vec/" class="md-nav__link">
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||
|
||
<span class="md-ellipsis">
|
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|
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|
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PersistentBitVec
|
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|
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|
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</span>
|
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|
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|
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|
||
<h1 id="numa-aware-worker-pools-for-merge">NUMA-aware worker pools for merge</h1>
|
||
<h2 id="problem">Problem</h2>
|
||
<p>The merge command's bottleneck is <code>compute_degrees</code> in <code>obidebruinj</code>: a random pointer-chase over 20–70 M node hash maps that saturates DRAM bandwidth. When multiple partition workers run concurrently, they contend for the shared memory bus, causing super-linear slowdown (measured: 0.016 µs/node solo → 0.95 µs/node with 4–5 concurrent workers, ×60 degradation).</p>
|
||
<p>Modern HPC nodes are multi-socket NUMA machines (observed: 2 sockets × 4 NUMA nodes × 24 cores = 192 cores). Cross-NUMA memory traffic compounds the contention:</p>
|
||
<ul>
|
||
<li>Full 192-core run: ~15 min/partition (×10 worse than M3 Mac)</li>
|
||
<li><code>taskset</code> restricted to 4 NUMA nodes (96 cores): ~90 s/partition</li>
|
||
<li>OAR job on 1 NUMA node (24 cores): ~80 s/partition, same throughput as 96 cores</li>
|
||
</ul>
|
||
<p><strong>Conclusion</strong>: the bottleneck is memory bandwidth per NUMA node, not core count. 24 cores on one NUMA node achieve the same throughput as 96 cores across four.</p>
|
||
<h2 id="strategy">Strategy</h2>
|
||
<p>Run N worker groups in parallel, one per NUMA node, each with its own Rayon thread pool whose threads are pinned to the NUMA node's CPUs. Linux's first-touch policy then places graph allocations on local DRAM automatically — no explicit NUMA allocator needed.</p>
|
||
<p>Expected throughput: N × single-NUMA throughput. On the 8-NUMA-node HPC: 8 × ~80 s = 9–10 min total instead of >60 min with the current single-pool approach.</p>
|
||
<h2 id="rayon-thread-pool-isolation">Rayon thread pool isolation</h2>
|
||
<p>Rayon provides <code>ThreadPool::install(|| { ... })</code>: any Rayon call (<code>par_iter</code>, <code>current_num_threads</code>, etc.) inside the closure uses <em>that</em> pool exclusively. Wrapping <code>merge_partition</code> in <code>pool.install()</code> redirects all downstream Rayon calls — including those in <code>debruijn.rs</code> and <code>partition.rs</code> — without touching those crates.</p>
|
||
<div class="highlight"><pre><span></span><code><span class="c1">// worker thread, assigned to NUMA pool `pool`</span>
|
||
<span class="n">pool</span><span class="p">.</span><span class="n">install</span><span class="p">(</span><span class="o">||</span><span class="w"> </span><span class="p">{</span>
|
||
<span class="w"> </span><span class="n">dst_partition</span><span class="p">.</span><span class="n">merge_partition</span><span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">srcs</span><span class="p">,</span><span class="w"> </span><span class="n">mode</span><span class="p">,</span><span class="w"> </span><span class="n">n_dst_genomes</span><span class="p">,</span><span class="w"> </span><span class="n">block_bits</span><span class="p">,</span><span class="w"> </span><span class="n">evidence</span><span class="p">)</span>
|
||
<span class="p">})</span>
|
||
</code></pre></div>
|
||
<p><code>rayon::current_num_threads()</code> inside <code>merge_partition</code> will return the pool size (e.g. 24), not the global thread count — which is the right value for buffer sizing.</p>
|
||
<h2 id="thread-pinning">Thread pinning</h2>
|
||
<p><code>ThreadPoolBuilder::spawn_handler</code> provides a hook executed for each thread at creation. Inside, <code>libc::sched_setaffinity</code> pins the thread to a CPU set:</p>
|
||
<div class="highlight"><pre><span></span><code><span class="kd">let</span><span class="w"> </span><span class="n">cpus</span><span class="p">:</span><span class="w"> </span><span class="nb">Vec</span><span class="o"><</span><span class="kt">usize</span><span class="o">></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">numa_node_cpus</span><span class="p">(</span><span class="n">node</span><span class="p">);</span><span class="w"> </span><span class="c1">// from /sys/devices/system/node/nodeN/cpulist</span>
|
||
<span class="n">rayon</span><span class="p">::</span><span class="n">ThreadPoolBuilder</span><span class="p">::</span><span class="n">new</span><span class="p">()</span>
|
||
<span class="w"> </span><span class="p">.</span><span class="n">num_threads</span><span class="p">(</span><span class="n">cpus</span><span class="p">.</span><span class="n">len</span><span class="p">())</span>
|
||
<span class="w"> </span><span class="p">.</span><span class="n">spawn_handler</span><span class="p">(</span><span class="k">move</span><span class="w"> </span><span class="o">|</span><span class="n">thread</span><span class="o">|</span><span class="w"> </span><span class="p">{</span>
|
||
<span class="w"> </span><span class="kd">let</span><span class="w"> </span><span class="k">mut</span><span class="w"> </span><span class="n">b</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="p">::</span><span class="n">thread</span><span class="p">::</span><span class="n">Builder</span><span class="p">::</span><span class="n">new</span><span class="p">();</span>
|
||
<span class="w"> </span><span class="n">std</span><span class="p">::</span><span class="n">thread</span><span class="p">::</span><span class="n">Builder</span><span class="p">::</span><span class="n">new</span><span class="p">().</span><span class="n">spawn</span><span class="p">(</span><span class="k">move</span><span class="w"> </span><span class="o">||</span><span class="w"> </span><span class="p">{</span>
|
||
<span class="w"> </span><span class="n">pin_to_cpus</span><span class="p">(</span><span class="o">&</span><span class="n">cpus</span><span class="p">);</span><span class="w"> </span><span class="c1">// sched_setaffinity via libc</span>
|
||
<span class="w"> </span><span class="n">thread</span><span class="p">.</span><span class="n">run</span><span class="p">()</span>
|
||
<span class="w"> </span><span class="p">})</span><span class="o">?</span><span class="p">;</span>
|
||
<span class="w"> </span><span class="nb">Ok</span><span class="p">(())</span>
|
||
<span class="w"> </span><span class="p">})</span>
|
||
<span class="w"> </span><span class="p">.</span><span class="n">build</span><span class="p">()</span><span class="o">?</span>
|
||
</code></pre></div>
|
||
<p>NUMA topology is read from <code>/sys/devices/system/node/node*/cpulist</code> — no <code>libnuma</code> dependency required. If the <code>numa</code> crate is linked, <code>numa_available()</code> / <code>numa_run_on_node()</code> are an alternative.</p>
|
||
<h2 id="memory-locality">Memory locality</h2>
|
||
<p>Linux allocates pages on the NUMA node of the thread that first writes them (first-touch policy). Once Rayon threads are pinned to node N, all graph data built by those threads lands on node N's DRAM. No changes to the allocator, no explicit <code>numa_alloc_onnode</code> calls.</p>
|
||
<h2 id="adaptive-spawn-criterion">Adaptive spawn criterion</h2>
|
||
<p>The current criterion uses <code>std::thread::available_parallelism()</code> (returns total cores = 192) and <code>max_workers = n_cores / 2</code>. With NUMA pools:</p>
|
||
<ul>
|
||
<li><code>n_cores</code> per pool = cores per NUMA node (e.g. 24)</li>
|
||
<li><code>max_workers</code> per pool = pool size / 2 (e.g. 12)</li>
|
||
<li>CPU efficiency is measured per pool, not globally</li>
|
||
</ul>
|
||
<p>Each NUMA group runs its own independent adaptive pool. Workers are distributed across NUMA groups round-robin or by workload (partition assignment can be pre-split by NUMA group index).</p>
|
||
<h2 id="required-changes">Required changes</h2>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>File</th>
|
||
<th>Change</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td><code>obikindex/src/merge.rs</code></td>
|
||
<td>Detect NUMA topology; build N <code>ThreadPool</code>s with pinned threads; assign each pre-spawned worker to a pool; wrap <code>merge_partition</code> in <code>pool.install()</code></td>
|
||
</tr>
|
||
<tr>
|
||
<td><code>obikindex/src/merge.rs</code></td>
|
||
<td>Replace <code>available_parallelism()</code> with per-NUMA core count for spawn criterion</td>
|
||
</tr>
|
||
<tr>
|
||
<td><code>obikpartitionner/src/merge_layer.rs</code></td>
|
||
<td>No change — <code>merge_partition</code> already works inside any Rayon context</td>
|
||
</tr>
|
||
<tr>
|
||
<td><code>obidebruinj/src/debruijn.rs</code></td>
|
||
<td>No change — <code>par_iter</code> and <code>current_num_threads</code> are pool-context-aware</td>
|
||
</tr>
|
||
<tr>
|
||
<td><code>obikpartitionner/src/partition.rs</code></td>
|
||
<td>No change — same reason</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<h2 id="platform-guard">Platform guard</h2>
|
||
<p>NUMA pinning is Linux-only. The fallback is the current single global pool:</p>
|
||
<div class="highlight"><pre><span></span><code><span class="cp">#[cfg(target_os = </span><span class="s">"linux"</span><span class="cp">)]</span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">build_numa_pools</span><span class="p">()</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nb">Option</span><span class="o"><</span><span class="nb">Vec</span><span class="o"><</span><span class="n">rayon</span><span class="p">::</span><span class="n">ThreadPool</span><span class="o">>></span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="o">..</span><span class="p">.</span><span class="w"> </span><span class="p">}</span>
|
||
|
||
<span class="cp">#[cfg(not(target_os = </span><span class="s">"linux"</span><span class="cp">))]</span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">build_numa_pools</span><span class="p">()</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nb">Option</span><span class="o"><</span><span class="nb">Vec</span><span class="o"><</span><span class="n">rayon</span><span class="p">::</span><span class="n">ThreadPool</span><span class="o">>></span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="nb">None</span><span class="w"> </span><span class="p">}</span>
|
||
</code></pre></div>
|
||
<p>When <code>build_numa_pools()</code> returns <code>None</code> (macOS, UMA, or single-socket), <code>merge.rs</code> uses the existing code path unchanged.</p>
|
||
<h2 id="open-questions">Open questions</h2>
|
||
<ul>
|
||
<li><strong>Partition assignment</strong>: split partitions by NUMA group up-front (static) or use a shared queue with per-group workers stealing from a common pool? Static split is simpler; stealing is better for load balance when partitions vary widely in size.</li>
|
||
<li><strong>Intra-NUMA adaptive criterion</strong>: with 24 cores and ~3–5 effective workers per NUMA node, the current marginal-gain criterion needs re-tuning or can be left as-is with per-pool <code>n_cores = 24</code>.</li>
|
||
<li><strong>I/O</strong>: partition data (unitig files) is on a shared filesystem. With 8 concurrent NUMA groups, I/O concurrency increases 8× — need to verify the filesystem (Lustre or local SSD) can absorb it without becoming the new bottleneck.</li>
|
||
</ul>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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