NodeJS using Cluster to run a function in parallel - node.js

Question

My server receives a list base64 image eg [img1,img2....] at an endpoint.
I have function getPoses(img) that takes an image and returns an object back.
I want to be able to map the images to getPoses() and get the object back for each image in Parallel.
I have tried Async.each function but I notice it's not really Parallel. Probably because it runs in a single thread/core.
I have also tried to use cluster, so that I can utilize all the cores to run getPoses() in multiple cores. But I feel stuck because, It's not clear to me how the master can obtain the poses found by workers.
I would be super gratefully for any help from the community.
Aync code:
//END POINT
app.post('/postImage' ,async function(req,res){
const imgBase64List = req.body.imgBase64.split('next');
const imgBase64IndexList = []
imgBase64List.map((imgBase64,index)=>{
imgBase64IndexList.push([index,imgBase64])
})
async.each(imgBase64IndexList , getPoseDetection , function(err){
if(err){
console.log("Error occured when feteching one of the pose")
console.log(err)
}else{
console.log('Sending Poses!')
res.send(JSON.stringify(poseDetectionResults))
}
})
});//End of app.post and asyn function
const getPoseDetection = async (imgBase64Index, callback) => {
//console.log('start');
const img = new Image();
img.src = imgBase64Index[1];
const imgIndex = imgBase64Index[0]
const canvas = createCanvas(img.width, img.height);
const ctx = canvas.getContext('2d');
ctx.drawImage(img, 0, 0);
const input = tf.browser.fromPixels(canvas);
const imageScaleFactor = 0.3;
const flipHorizontal = false;
const outputStride = 8;
const poses = await net.estimateMultiplePoses(input, {
flipHorizontal: false,
maxDetections: 2,
minPoseConfidence: 0.15,
minPartConfidence:0.1,
nmsRadius:20,
});
boundingBoxes = [] //reset the boundingBoxese
poses.forEach(pose => {
var box = posenet.getBoundingBoxPoints(pose.keypoints);
boundingBoxes.push(box)
});
poseDetectionResults[imgIndex] = {detectionList:poses}
//console.log(poses)
}
Thank You