I've now tested v8 6.1 using the Node V9 canary build, see the details here 
<https://github.com/winksaville/test-nn-ts/tree/nodev9>. The summary is 
that Nodev9 for both ES5 and ES6 run approximately the same speed at 
820,000 eps which is about *1.7x faster* than Nodev8 using ES5!

Kudo's to the V8 team!!


On Monday, June 12, 2017 at 9:36:01 PM UTC-7, Benedikt Meurer wrote:
>
> With V8 5.8, that sounds possible. In 5.8 modern JavaScript (i.e. ES2015 
> and later) is sent to the new compiler pipeline (based on Ignition and 
> TurboFan), whereas classic JavaScript is sent to the old compiler pipeline 
> (based on FullCodegen and Crankshaft). JavaScript code is strictly divided 
> by this and so you have a two-world view problem. In  your case you 
> probably benefit from having the JIT inline the class constructor for you 
> into call sites, but with ES6 classes and V8 5.8, that's only possible if 
> the call site also lives in the Ignition+TurboFan world, which is probably 
> not the case and hence you see a significant slowdown already. All of these 
> problems disappear with V8 5.9/6.0, which is what Node 8 will upgrade to 
> eventually.
>
> HTH,
> Benedikt
>
> On Tue, Jun 13, 2017 at 12:18 AM Wink Saville <[email protected] 
> <javascript:>> wrote:
>
>> I've created a simple NeuralNet program 
>> <https://github.com/winksaville/test-nn-ts> in TypeScript v2.3.4 running 
>> on node v8.1.0 and v8 v5.8.283.41:
>>
>> $ npm version
>>> { 'test-nn-ts': '0.1.0',
>>>   npm: '4.6.1',
>>>   ares: '1.12.0',
>>>   cldr: '31.0.1',
>>>   http_parser: '2.7.1',
>>>   icu: '59.1',
>>>   modules: '57',
>>>   node: '8.1.0',
>>>   openssl: '1.0.2l',
>>>   tz: '2017b',
>>>   unicode: '9.0',
>>>   uv: '1.12.0',
>>>   v8: '5.8.283.41',
>>>   zlib: '1.2.11' }
>>
>>
>> When I target es6 the program runs 6x slower than if target es5.
>>
>> Here is the output when targeting es5:
>>
>> $ yarn runit 1000000
>>> yarn runit v0.24.6
>>> $ yarn build && yarn doit 1000000
>>> yarn build v0.24.6
>>> $ tsc -p src/tsconfig.json 
>>> Done in 2.02s.
>>> yarn doit v0.24.6
>>> $ node build/test-nn.js 1000000
>>> Epoch=1,000,000 Error=4.63e-7 time=2.06s eps=484,784
>>> Pat Input0 Input1 Target0 Output0
>>> 0 0 0 0 0.000448827454864753
>>> 1 1 0 1 0.9995444354288804
>>> 2 0 1 1 0.9995445564977191
>>> 3 1 1 0 0.0005558910332638871
>>> Done in 2.24s.
>>> Done in 4.63s.
>>
>>
>> And here is the output targeting es6:
>>
>> $ yarn runit 1000000
>>> yarn runit v0.24.6
>>> $ yarn build && yarn doit 1000000
>>> yarn build v0.24.6
>>> $ tsc -p src/tsconfig.json 
>>> Done in 1.99s.
>>> yarn doit v0.24.6
>>> $ node build/test-nn.js 1000000
>>> Epoch=1,000,000 Error=4.63e-7 time=13.29s eps=75,239
>>> Pat Input0 Input1 Target0 Output0
>>> 0 0 0 0 0.000448827454864753
>>> 1 1 0 1 0.9995444354288804
>>> 2 0 1 1 0.9995445564977191
>>> 3 1 1 0 0.0005558910332638871
>>> Done in 13.47s.
>>> Done in 15.84s.
>>
>>
>> Here is a snippet of the beginning of the NeuralNet.js targeting es5:
>>
>> var Neuron_1 = require("./Neuron");
>>> var DBG = false;
>>> var Debug_1 = require("./Debug");
>>> var NeuralNet = (function () {
>>>     function NeuralNet(num_in_neurons, num_hidden_layers, 
>>> num_out_neurons) {
>>>         if (DBG)
>>>             Debug_1.default("ctor:+ in_neurons=" + num_in_neurons + " 
>>> hidden_layers=" + num_hidden_layers + " out_neurons=" + num_out_neurons);
>>>         this.max_layers = 2; // We always have an input and output layer
>>>         this.max_layers += num_hidden_layers; // Add num_hidden layers
>>>         this.out_layer = this.max_layers - 1; // last one is out_layer
>>>         this.last_hidden = 0; // No hidden layers yet
>>>         this.points = 0; // No points yet
>>>         this.error = 0; // No errors yet
>>>         this.learning_rate = 0.5; // Learning rate aka eta
>>>         this.momentum_factor = 0.9; // momemtum factor aka alpha
>>>         this.layers = new Array(this.max_layers);
>>>         // Create the input and output layers
>>>         this.create_layer(0, num_in_neurons);
>>>         this.create_layer(this.out_layer, num_out_neurons);
>>>         if (DBG)
>>>             Debug_1.default("ctor:-");
>>>     }
>>>     NeuralNet.prototype.create_layer = function (layer_index, 
>>> num_neurons) {
>>>         if (DBG)
>>>             Debug_1.default("create_layer:+ layer_index=" + layer_index 
>>> + " num_neurons=" + num_neurons);
>>>         this.layers[layer_index] = new Array(num_neurons);
>>>         if (DBG)
>>>             Debug_1.default("create_layer:- layer_index=" + layer_index 
>>> + " num_neurons=" + num_neurons);
>>>     };
>>
>>
>>
>> And here is a snippet targeting es6:
>>
>> const Neuron_1 = require("./Neuron");
>>> const DBG = false;
>>> const Debug_1 = require("./Debug");
>>> class NeuralNet {
>>>     constructor(num_in_neurons, num_hidden_layers, num_out_neurons) {
>>>         if (DBG)
>>>             Debug_1.default(`ctor:+ in_neurons=${num_in_neurons} 
>>> hidden_layers=${num_hidden_layers} out_neurons=${num_out_neurons}`);
>>>         this.max_layers = 2; // We always have an input and output layer
>>>         this.max_layers += num_hidden_layers; // Add num_hidden layers
>>>         this.out_layer = this.max_layers - 1; // last one is out_layer
>>>         this.last_hidden = 0; // No hidden layers yet
>>>         this.points = 0; // No points yet
>>>         this.error = 0; // No errors yet
>>>         this.learning_rate = 0.5; // Learning rate aka eta
>>>         this.momentum_factor = 0.9; // momemtum factor aka alpha
>>>         this.layers = new Array(this.max_layers);
>>>         // Create the input and output layers
>>>         this.create_layer(0, num_in_neurons);
>>>         this.create_layer(this.out_layer, num_out_neurons);
>>>         if (DBG)
>>>             Debug_1.default("ctor:-");
>>>     }
>>>     create_layer(layer_index, num_neurons) {
>>>         if (DBG)
>>>             Debug_1.default(`create_layer:+ layer_index=${layer_index} 
>>> num_neurons=${num_neurons}`);
>>>         this.layers[layer_index] = new Array(num_neurons);
>>>         if (DBG)
>>>             Debug_1.default(`create_layer:- layer_index=${layer_index} 
>>> num_neurons=${num_neurons}`);
>>>     }
>>
>>
>> Have I done something wrong or is this expected?
>>
>> -- Wink
>>
>> -- 
>> -- 
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