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Reduce memory pressure by double-buffered PageRank (#520)

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Summary:
This commit switches to a double-buffered PageRank implementation. When
benchmarked on `ipfs/js-ipfs`, the critical section improves from
3059 ms to 2433 ms (79.5% of original), and peak heap usage drops from
342 MB to 207 MB. (Tested non-rigorously in Chrome 67.)

Test Plan:
Existing unit tests for `sparseMarkovChainAction`,
`findStationaryDistribution`, and `pagerank` are sufficient.

wchargin-branch: pagerank-dbuf
This commit is contained in:
William Chargin 2018-07-24 17:51:40 -07:00 committed by GitHub
parent 4435a3cfad
commit 77fa29320c
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GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 21 additions and 12 deletions
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33
src/core/attribution/markovChain.js
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@ -73,20 +73,28 @@ export function uniformDistribution(n: number): Distribution {
return new Float64Array(n).fill(1 / n);
}
export function sparseMarkovChainAction(
function sparseMarkovChainActionInto(
chain: SparseMarkovChain,
pi: Distribution
): Distribution {
const result = new Float64Array(pi.length);
input: Distribution,
output: Distribution
): void {
chain.forEach(({neighbor, weight}, dst) => {
const inDegree = neighbor.length; // (also `weight.length`)
let probability = 0;
for (let i = 0; i < inDegree; i++) {
const src = neighbor[i];
probability += pi[src] * weight[i];
probability += input[src] * weight[i];
}
result[dst] = probability;
output[dst] = probability;
});
}
export function sparseMarkovChainAction(
chain: SparseMarkovChain,
pi: Distribution
): Distribution {
const result = new Float64Array(pi.length);
sparseMarkovChainActionInto(chain, pi, result);
return result;
}
@ -98,7 +106,8 @@ function* findStationaryDistributionGenerator(
+maxIterations: number,
|}
): Generator<void, Distribution, void> {
let r0 = uniformDistribution(chain.length);
let pi = uniformDistribution(chain.length);
let scratch = new Float64Array(pi.length);
function computeDelta(pi0, pi1) {
let maxDelta = -Infinity;
// Here, we assume that `pi0.nodeOrder` and `pi1.nodeOrder` are the
@ -115,12 +124,12 @@ function* findStationaryDistributionGenerator(
if (options.verbose) {
console.log(`[${iteration}] FAILED to converge`);
}
return r0;
return pi;
}
iteration++;
const r1 = sparseMarkovChainAction(chain, r0);
const delta = computeDelta(r0, r1);
r0 = r1;
sparseMarkovChainActionInto(chain, pi, scratch);
const delta = computeDelta(pi, scratch);
[scratch, pi] = [pi, scratch];
if (options.verbose) {
console.log(`[${iteration}] delta = ${delta}`);
}
@ -128,7 +137,7 @@ function* findStationaryDistributionGenerator(
if (options.verbose) {
console.log(`[${iteration}] CONVERGED`);
}
return r0;
return pi;
}
yield;
}