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Sasho Nikolov
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EDIT (March 2014) I should say that I have since worked more on algorithms for MapReduce-type models of computation, and I feel like I was being overly negative. The Divide-Compress-Conquer technique I talk about below is surprisingly versatile, and can be the basis of algorithms which I think are non-trivial and interesting.


Let me offer an answer that will be much inferior to Mike's in terms of comprehensiveness, but from a model of computation/algorithmic theory standpoint.

Why there is excitement: MapReduce interleaves parallel and sequential computation; each processor has access to a nontrivial chunk (e.g. $O(n^\epsilon)$) of the input and can perform a nontrivial operation on it; that is very much unlike PRAM models and seems like an interesting idea that might lead to new algorithmic techniques. In particular, some problems can be solved in few (constant in input size) rounds of computation, while no nontrivial problem can be solved in PRAM in $o(\log n)$ time.

Why the model is getting slightly frustrating for me: The only algorithmic technique that seems to work to get $O(1)$ rounds algorithms and is somewhat new is the following

  • Partition the problem instance (often randomly)
  • Do some computation on each partition in parallel and represent the result of the computation compactly
  • Combine all the compactly represented subproblem solutions on a single processor and finish the computation there

Very simple example of the technique: compute the sum of $n$ numbers. Each processor has $O(\sqrt{n})$ of the array, and computes the sum of that portion. Then all the $\sqrt{n}$ sums can be combined on a single processor to compute the total sum. A slightly more interesting exercise is to compute all prefix sums this way (of course in that case the output has to be represented in a distributed way). Or compute a spanning tree of a dense graph.

Now, I think this actually is an interesting twist on divide and conquer, the twist being that after the divide stage you need to compress subproblem solutions so that a single processor can conquer. However, this really seems to be the only technique we've come up with so far. It fails on problems with sparse graphs, like sparse connectivity for example. Contrast this with the streaming model, which led to a wealth of new ideas, like the ingenuous sampling algorithm of Flajolet and Martin, the deterministic pairing algorithm of Misra and Gries, the power of simple sketching techniques, etc.

As a programming paradigm, map reduce has been very successful. My comments regard map reduce as an interesting model of computation. Good theoretical models are a little bit odd. If they follow reality too closely they are unwieldy, but more importantly, (to borrow a term from machine learning) theorems proved for models which are too specific do not generalize, i.e. do not hold in other models. That's why we want to abstract away as much detail as possible, while still leaving enough to challenge us to come up with novel algorithms. Finally, those new ideas should be able to eventually find their way back to the real world. PRAM is one unrealistic model which led to interesting ideas but those ideas proved to be rarely applicable to real world parallel computation. On the other hand, streaming is also unrealistic, but it inspired algorithmic ideas which are actually employed in the real world. See count-min sketch. Sketching techniques are in fact also used in systems based on map reduce.

Let me offer an answer that will be much inferior to Mike's in terms of comprehensiveness, but from a model of computation/algorithmic theory standpoint.

Why there is excitement: MapReduce interleaves parallel and sequential computation; each processor has access to a nontrivial chunk (e.g. $O(n^\epsilon)$) of the input and can perform a nontrivial operation on it; that is very much unlike PRAM models and seems like an interesting idea that might lead to new algorithmic techniques. In particular, some problems can be solved in few (constant in input size) rounds of computation, while no nontrivial problem can be solved in PRAM in $o(\log n)$ time.

Why the model is getting slightly frustrating for me: The only algorithmic technique that seems to work to get $O(1)$ rounds algorithms and is somewhat new is the following

  • Partition the problem instance (often randomly)
  • Do some computation on each partition in parallel and represent the result of the computation compactly
  • Combine all the compactly represented subproblem solutions on a single processor and finish the computation there

Very simple example of the technique: compute the sum of $n$ numbers. Each processor has $O(\sqrt{n})$ of the array, and computes the sum of that portion. Then all the $\sqrt{n}$ sums can be combined on a single processor to compute the total sum. A slightly more interesting exercise is to compute all prefix sums this way (of course in that case the output has to be represented in a distributed way). Or compute a spanning tree of a dense graph.

Now, I think this actually is an interesting twist on divide and conquer, the twist being that after the divide stage you need to compress subproblem solutions so that a single processor can conquer. However, this really seems to be the only technique we've come up with so far. It fails on problems with sparse graphs, like sparse connectivity for example. Contrast this with the streaming model, which led to a wealth of new ideas, like the ingenuous sampling algorithm of Flajolet and Martin, the deterministic pairing algorithm of Misra and Gries, the power of simple sketching techniques, etc.

As a programming paradigm, map reduce has been very successful. My comments regard map reduce as an interesting model of computation. Good theoretical models are a little bit odd. If they follow reality too closely they are unwieldy, but more importantly, (to borrow a term from machine learning) theorems proved for models which are too specific do not generalize, i.e. do not hold in other models. That's why we want to abstract away as much detail as possible, while still leaving enough to challenge us to come up with novel algorithms. Finally, those new ideas should be able to eventually find their way back to the real world. PRAM is one unrealistic model which led to interesting ideas but those ideas proved to be rarely applicable to real world parallel computation. On the other hand, streaming is also unrealistic, but it inspired algorithmic ideas which are actually employed in the real world. See count-min sketch. Sketching techniques are in fact also used in systems based on map reduce.

EDIT (March 2014) I should say that I have since worked more on algorithms for MapReduce-type models of computation, and I feel like I was being overly negative. The Divide-Compress-Conquer technique I talk about below is surprisingly versatile, and can be the basis of algorithms which I think are non-trivial and interesting.


Let me offer an answer that will be much inferior to Mike's in terms of comprehensiveness, but from a model of computation/algorithmic theory standpoint.

Why there is excitement: MapReduce interleaves parallel and sequential computation; each processor has access to a nontrivial chunk (e.g. $O(n^\epsilon)$) of the input and can perform a nontrivial operation on it; that is very much unlike PRAM models and seems like an interesting idea that might lead to new algorithmic techniques. In particular, some problems can be solved in few (constant in input size) rounds of computation, while no nontrivial problem can be solved in PRAM in $o(\log n)$ time.

Why the model is getting slightly frustrating for me: The only algorithmic technique that seems to work to get $O(1)$ rounds algorithms and is somewhat new is the following

  • Partition the problem instance (often randomly)
  • Do some computation on each partition in parallel and represent the result of the computation compactly
  • Combine all the compactly represented subproblem solutions on a single processor and finish the computation there

Very simple example of the technique: compute the sum of $n$ numbers. Each processor has $O(\sqrt{n})$ of the array, and computes the sum of that portion. Then all the $\sqrt{n}$ sums can be combined on a single processor to compute the total sum. A slightly more interesting exercise is to compute all prefix sums this way (of course in that case the output has to be represented in a distributed way). Or compute a spanning tree of a dense graph.

Now, I think this actually is an interesting twist on divide and conquer, the twist being that after the divide stage you need to compress subproblem solutions so that a single processor can conquer. However, this really seems to be the only technique we've come up with so far. It fails on problems with sparse graphs, like sparse connectivity for example. Contrast this with the streaming model, which led to a wealth of new ideas, like the ingenuous sampling algorithm of Flajolet and Martin, the deterministic pairing algorithm of Misra and Gries, the power of simple sketching techniques, etc.

As a programming paradigm, map reduce has been very successful. My comments regard map reduce as an interesting model of computation. Good theoretical models are a little bit odd. If they follow reality too closely they are unwieldy, but more importantly, (to borrow a term from machine learning) theorems proved for models which are too specific do not generalize, i.e. do not hold in other models. That's why we want to abstract away as much detail as possible, while still leaving enough to challenge us to come up with novel algorithms. Finally, those new ideas should be able to eventually find their way back to the real world. PRAM is one unrealistic model which led to interesting ideas but those ideas proved to be rarely applicable to real world parallel computation. On the other hand, streaming is also unrealistic, but it inspired algorithmic ideas which are actually employed in the real world. See count-min sketch. Sketching techniques are in fact also used in systems based on map reduce.

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Raphael
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Let me offer an answer that will be much inferior to Mike's in terms of comprehensiveness, but from a model of computation/algorithmic theory standpoint.

Why there is excitement: MapReduce interleaves parallel and sequential computation; each processor has access to a nontrivial chunk (e.g. $O(n^\epsilon)$) of the input and can perform a nontrivial operation on it; that is very much unlike PRAM models and seems like an interesting idea that might lead to new algorithmic techniques. In particular, some problems can be solved in few (constant in input size) rounds of computation, while no nontrivial problem can be solved in PRAM in $o(\log n)$ time.

Why the model is getting slightly frustrating for me: The only algorithmic technique that seems to work to get $O(1)$ rounds algorithms and is somewhat new is the following

  • Partition the problem instance (often randomly)
  • Do some computation on each partition in parallel and represent the result of the computation compactly
  • Combine all the compactly represented subproblem solutions on a single processor and finish the computation there

Very simple example of the technique: compute the sum of $n$ numbers. Each processor has $O(\sqrt{n})$ of the array, and computes the sum of that portion. Then all the $\sqrt{n}$ sums can be combined on a single processor to compute the total sum. A slightly more interesting exercise is to compute all prefix sums this way (of course in that case the output has to be represented in a distributed way). Or compute a spanning tree of a dense graph.

Now, I think this actually is an interesting twist on divide and conquer, the twist being that after the divide stage you need to compress subproblem solutions so that a single processor can conquer. However, this really seems to be the only technique we've come up with so far. It fails on problems with sparse graphs, like sparse connectivity for example. Contrast this with the streaming model, which led to a wealth of new ideas, like the ingenuous sampling algorithm of Flajolet and Martin, the deterministic pairing algorithm of Misra and Gries, the power of simple sketching techniques, etc.

Edit: As a programming paradigm, map reduce has been very successful. My comments regard map reduce as an interesting model of computation. Good theoretical models are a little bit odd. If they follow reality too closely they are unwieldy, but more importantly, (to borrow a term from machine learning) theorems proved for models which are too specific do not generalize, i.e. do not hold in other models. That's why we want to abstract away as much detail as possible, while still leaving enough to challenge us to come up with novel algorithms. Finally, those new ideas should be able to eventually find their way back to the real world. PRAM is one unrealistic model which led to interesting ideas but those ideas proved to be rarely applicable to real world parallel computation. On the other hand, streaming is also unrealistic, but it inspired algorithmic ideas which are actually employed in the real world. See https://sites.google.com/site/countminsketch/homecount-min sketch. Sketching techniques are in fact also used in systems based on map reduce.

Let me offer an answer that will be much inferior to Mike's in terms of comprehensiveness, but from a model of computation/algorithmic theory standpoint.

Why there is excitement: MapReduce interleaves parallel and sequential computation; each processor has access to a nontrivial chunk (e.g. $O(n^\epsilon)$) of the input and can perform a nontrivial operation on it; that is very much unlike PRAM models and seems like an interesting idea that might lead to new algorithmic techniques. In particular, some problems can be solved in few (constant in input size) rounds of computation, while no nontrivial problem can be solved in PRAM in $o(\log n)$ time.

Why the model is getting slightly frustrating for me: The only algorithmic technique that seems to work to get $O(1)$ rounds algorithms and is somewhat new is the following

  • Partition the problem instance (often randomly)
  • Do some computation on each partition in parallel and represent the result of the computation compactly
  • Combine all the compactly represented subproblem solutions on a single processor and finish the computation there

Very simple example of the technique: compute the sum of $n$ numbers. Each processor has $O(\sqrt{n})$ of the array, and computes the sum of that portion. Then all the $\sqrt{n}$ sums can be combined on a single processor to compute the total sum. A slightly more interesting exercise is to compute all prefix sums this way (of course in that case the output has to be represented in a distributed way). Or compute a spanning tree of a dense graph.

Now, I think this actually is an interesting twist on divide and conquer, the twist being that after the divide stage you need to compress subproblem solutions so that a single processor can conquer. However, this really seems to be the only technique we've come up with so far. It fails on problems with sparse graphs, like sparse connectivity for example. Contrast this with the streaming model, which led to a wealth of new ideas, like the ingenuous sampling algorithm of Flajolet and Martin, the deterministic pairing algorithm of Misra and Gries, the power of simple sketching techniques, etc.

Edit: As a programming paradigm, map reduce has been very successful. My comments regard map reduce as an interesting model of computation. Good theoretical models are a little bit odd. If they follow reality too closely they are unwieldy, but more importantly, (to borrow a term from machine learning) theorems proved for models which are too specific do not generalize, i.e. do not hold in other models. That's why we want to abstract away as much detail as possible, while still leaving enough to challenge us to come up with novel algorithms. Finally, those new ideas should be able to eventually find their way back to the real world. PRAM is one unrealistic model which led to interesting ideas but those ideas proved to be rarely applicable to real world parallel computation. On the other hand, streaming is also unrealistic, but it inspired algorithmic ideas which are actually employed in the real world. See https://sites.google.com/site/countminsketch/home. Sketching techniques are in fact also used in systems based on map reduce.

Let me offer an answer that will be much inferior to Mike's in terms of comprehensiveness, but from a model of computation/algorithmic theory standpoint.

Why there is excitement: MapReduce interleaves parallel and sequential computation; each processor has access to a nontrivial chunk (e.g. $O(n^\epsilon)$) of the input and can perform a nontrivial operation on it; that is very much unlike PRAM models and seems like an interesting idea that might lead to new algorithmic techniques. In particular, some problems can be solved in few (constant in input size) rounds of computation, while no nontrivial problem can be solved in PRAM in $o(\log n)$ time.

Why the model is getting slightly frustrating for me: The only algorithmic technique that seems to work to get $O(1)$ rounds algorithms and is somewhat new is the following

  • Partition the problem instance (often randomly)
  • Do some computation on each partition in parallel and represent the result of the computation compactly
  • Combine all the compactly represented subproblem solutions on a single processor and finish the computation there

Very simple example of the technique: compute the sum of $n$ numbers. Each processor has $O(\sqrt{n})$ of the array, and computes the sum of that portion. Then all the $\sqrt{n}$ sums can be combined on a single processor to compute the total sum. A slightly more interesting exercise is to compute all prefix sums this way (of course in that case the output has to be represented in a distributed way). Or compute a spanning tree of a dense graph.

Now, I think this actually is an interesting twist on divide and conquer, the twist being that after the divide stage you need to compress subproblem solutions so that a single processor can conquer. However, this really seems to be the only technique we've come up with so far. It fails on problems with sparse graphs, like sparse connectivity for example. Contrast this with the streaming model, which led to a wealth of new ideas, like the ingenuous sampling algorithm of Flajolet and Martin, the deterministic pairing algorithm of Misra and Gries, the power of simple sketching techniques, etc.

As a programming paradigm, map reduce has been very successful. My comments regard map reduce as an interesting model of computation. Good theoretical models are a little bit odd. If they follow reality too closely they are unwieldy, but more importantly, (to borrow a term from machine learning) theorems proved for models which are too specific do not generalize, i.e. do not hold in other models. That's why we want to abstract away as much detail as possible, while still leaving enough to challenge us to come up with novel algorithms. Finally, those new ideas should be able to eventually find their way back to the real world. PRAM is one unrealistic model which led to interesting ideas but those ideas proved to be rarely applicable to real world parallel computation. On the other hand, streaming is also unrealistic, but it inspired algorithmic ideas which are actually employed in the real world. See count-min sketch. Sketching techniques are in fact also used in systems based on map reduce.

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Sasho Nikolov
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Let me offer an answer that will be much inferior to Mike's in terms of comprehensiveness, but from a model of computation/algorithmic theory standpoint.

Why there is excitement: MapReduce interleaves parallel and sequential computation; each processor has access to a nontrivial chunk (e.g. $O(n^\epsilon)$) of the input and can perform a nontrivial operation on it; that is very much unlike PRAM models and seems like an interesting idea that might lead to new algorithmic techniques. In particular, some problems can be solved in few (constant in input size) rounds of computation, while no nontrivial problem can be solved in PRAM in $o(\log n)$ time.

Why the model is getting slightly frustrating for me: The only algorithmic technique that seems to work to get $O(1)$ rounds algorithms and is somewhat new is the following

  • Partition the problem instance (often randomly)
  • Do some computation on each partition in parallel and represent the result of the computation compactly
  • Combine all the compactly represented subproblem solutions on a single processor and finish the computation there

Very simple example of the technique: compute the sum of $n$ numbers. Each processor has $O(\sqrt{n})$ of the array, and computes the sum of that portion. Then all the $\sqrt{n}$ sums can be combined on a single processor to compute the total sum. A slightly more interesting exercise is to compute all prefix sums this way (of course in that case the output has to be represented in a distributed way). Or compute a spanning tree of a dense graph.

Now, I think this actually is an interesting twist on divide and conquer, the twist being that after the divide stage you need to compress subproblem solutions so that a single processor can conquer. However, this really seems to be the only technique we've come up with so far. It fails on problems with sparse graphs, like sparse connectivity for example. Contrast this with the streaming model, which led to a wealth of new ideas, like the ingenuous sampling algorithm of Flajolet and Martin, the deterministic pairing algorithm of Misra and Gries, the power of simple sketching techniques, etc.

Edit: As a programming paradigm, map reduce has been very successful. My comments regard map reduce as an interesting model of computation. Good theoretical models are a little bit odd. If they follow reality too closely they are unwieldy, but more importantly, (to borrow a term from machine learning) theorems proved for models which are too specific do not generalize, i.e. do not hold in other models. That's why we want to abstract away as much detail as possible, while still leaving enough to challenge us to come up with novel algorithms. Finally, those new ideas should be able to eventually find their way back to the real world. PRAM is one unrealistic model which led to interesting ideas but those ideas proved to be rarely applicable to real world parallel computation. On the other hand, streaming is also unrealistic, but it inspired algorithmic ideas which are actually employed in the real world. See https://sites.google.com/site/countminsketch/home. Sketching techniques are in fact also used in systems based on map reduce.

Let me offer an answer that will be much inferior to Mike's in terms of comprehensiveness, but from a model of computation/algorithmic theory standpoint.

Why there is excitement: MapReduce interleaves parallel and sequential computation; each processor has access to a nontrivial chunk (e.g. $O(n^\epsilon)$) of the input and can perform a nontrivial operation on it; that is very much unlike PRAM models and seems like an interesting idea that might lead to new algorithmic techniques. In particular, some problems can be solved in few (constant in input size) rounds of computation, while no nontrivial problem can be solved in PRAM in $o(\log n)$ time.

Why the model is getting slightly frustrating for me: The only algorithmic technique that seems to work to get $O(1)$ rounds algorithms and is somewhat new is the following

  • Partition the problem instance (often randomly)
  • Do some computation on each partition in parallel and represent the result of the computation compactly
  • Combine all the compactly represented subproblem solutions on a single processor and finish the computation there

Very simple example of the technique: compute the sum of $n$ numbers. Each processor has $O(\sqrt{n})$ of the array, and computes the sum of that portion. Then all the $\sqrt{n}$ sums can be combined on a single processor to compute the total sum. A slightly more interesting exercise is to compute all prefix sums this way (of course in that case the output has to be represented in a distributed way). Or compute a spanning tree of a dense graph.

Now, I think this actually is an interesting twist on divide and conquer, the twist being that after the divide stage you need to compress subproblem solutions so that a single processor can conquer. However, this really seems to be the only technique we've come up with so far. It fails on problems with sparse graphs, like sparse connectivity for example. Contrast this with the streaming model, which led to a wealth of new ideas, like the ingenuous sampling algorithm of Flajolet and Martin, the deterministic pairing algorithm of Misra and Gries, the power of simple sketching techniques, etc.

Let me offer an answer that will be much inferior to Mike's in terms of comprehensiveness, but from a model of computation/algorithmic theory standpoint.

Why there is excitement: MapReduce interleaves parallel and sequential computation; each processor has access to a nontrivial chunk (e.g. $O(n^\epsilon)$) of the input and can perform a nontrivial operation on it; that is very much unlike PRAM models and seems like an interesting idea that might lead to new algorithmic techniques. In particular, some problems can be solved in few (constant in input size) rounds of computation, while no nontrivial problem can be solved in PRAM in $o(\log n)$ time.

Why the model is getting slightly frustrating for me: The only algorithmic technique that seems to work to get $O(1)$ rounds algorithms and is somewhat new is the following

  • Partition the problem instance (often randomly)
  • Do some computation on each partition in parallel and represent the result of the computation compactly
  • Combine all the compactly represented subproblem solutions on a single processor and finish the computation there

Very simple example of the technique: compute the sum of $n$ numbers. Each processor has $O(\sqrt{n})$ of the array, and computes the sum of that portion. Then all the $\sqrt{n}$ sums can be combined on a single processor to compute the total sum. A slightly more interesting exercise is to compute all prefix sums this way (of course in that case the output has to be represented in a distributed way). Or compute a spanning tree of a dense graph.

Now, I think this actually is an interesting twist on divide and conquer, the twist being that after the divide stage you need to compress subproblem solutions so that a single processor can conquer. However, this really seems to be the only technique we've come up with so far. It fails on problems with sparse graphs, like sparse connectivity for example. Contrast this with the streaming model, which led to a wealth of new ideas, like the ingenuous sampling algorithm of Flajolet and Martin, the deterministic pairing algorithm of Misra and Gries, the power of simple sketching techniques, etc.

Edit: As a programming paradigm, map reduce has been very successful. My comments regard map reduce as an interesting model of computation. Good theoretical models are a little bit odd. If they follow reality too closely they are unwieldy, but more importantly, (to borrow a term from machine learning) theorems proved for models which are too specific do not generalize, i.e. do not hold in other models. That's why we want to abstract away as much detail as possible, while still leaving enough to challenge us to come up with novel algorithms. Finally, those new ideas should be able to eventually find their way back to the real world. PRAM is one unrealistic model which led to interesting ideas but those ideas proved to be rarely applicable to real world parallel computation. On the other hand, streaming is also unrealistic, but it inspired algorithmic ideas which are actually employed in the real world. See https://sites.google.com/site/countminsketch/home. Sketching techniques are in fact also used in systems based on map reduce.

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Sasho Nikolov
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