Parallel execution with StackExchange.Redis?

Currently, your code is using the synchronous API (StringSet), and is being loaded by 10 threads concurrently. This will present no appreciable challenge to SE.Redis - it works just fine here. I suspect that it genuinely is a timeout where the server has taken longer than you would like to process some of the data, most likely also related to the server's allocator. One option, then, is to simply increase the timeout a bit. Not a lot... try 5 seconds instead of the default 1 second. Likely, most of the operations are working very fast anyway.

With regards to speeding it up: one option here is to not wait - i.e. keep pipelining data. If you are content not to check every single message for an error state, then one simple way to do this is to add , flags: CommandFlags.FireAndForget to the end of your StringSet call. In my local testing, this sped up the 1M example by 25% (and I suspect a lot of the rest of the time is actually spent in string serialization).

The biggest problem I had with the 10M example was simply the overhead of working with the 10M example - especially since this takes huge amounts of memory for both the redis-server and the application, which (to emulate your setup) are on the same machine. This creates competing memory pressure, with GC pauses etc in the managed code. But perhaps more importantly: it simply takes forever to start doing anything. Consequently, I refactored the code to use parallel yield return generators rather than a single list. For example:

    static IEnumerable<Person> InventPeople(int seed, int count)
    {
        for(int i = 0; i < count; i++)
        {
            int f = 1 + seed + i;
            var item = new Person
            {
                Id = f,
                Name = Path.GetRandomFileName().Replace(".", "").Substring(0, appRandom.Value.Next(3, 6)) + " " + Path.GetRandomFileName().Replace(".", "").Substring(0, new Random(Guid.NewGuid().GetHashCode()).Next(3, 6)),
                Age = f % 90,
                Friends = ParallelEnumerable.Range(0, 100).Select(n => appRandom.Value.Next(1, f)).ToArray()
            };
            yield return item;
        }
    }

    static IEnumerable<T> Batchify<T>(this IEnumerable<T> source, int count)
    {
        var list = new List<T>(count);
        foreach(var item in source)
        {
            list.Add(item);
            if(list.Count == count)
            {
                foreach (var x in list) yield return x;
                list.Clear();
            }
        }
        foreach (var item in list) yield return item;
    }

with:

foreach (var element in InventPeople(PER_THREAD * counter1, PER_THREAD).Batchify(1000))

Here, the purpose of Batchify is to ensure that we aren't helping the server too much by taking appreciable time between each operation - the data is invented in batches of 1000 and each batch is made available very quickly.

I was also concerned about JSON performance, so I switched to JIL:

    public static string ToJSON<T>(this T obj)
    {
        return Jil.JSON.Serialize<T>(obj);
    }

and then just for fun, I moved the JSON work into the batching (so that the actual processing loops :

 foreach (var element in InventPeople(PER_THREAD * counter1, PER_THREAD)
     .Select(x => new { x.Id, Json = x.ToJSON() }).Batchify(1000))

This got the times down a bit more, so I can load 10M in 3 minutes 57 seconds, a rate of 42,194 rops. Most of this time is actually local processing inside the application. If I change it so that each thread loads the same item ITEMS / THREADS times, then this changes to 1 minute 48 seconds - a rate of 92,592 rops.

I'm not sure if I've answered anything really, but the short version might be simply "try a longer timeout; consider using fire-and-forget).