Thanks, we all already know about that paper, and I discussed it a few times in some of my posts. I also implemented it (even in my public Python kangaroo script) and it indeed brings C to ~ 1.25 - 1.45 but at the expense of much more complex logic, since we can have cycles in the side by side walks, which need to be handled. In addition, that method completely ignores the cost of having to create new starting points, which is not something to be ignored (in fact, is very very costly). This problem is also mentioned in the 3 & 4 kangaroo methods paper (which was actually a follow up to the paper you cited). There is also a more recent "fix" to the Gaudry-Schost method (but it is written in Chinese) which brings down C a little bit more, by refining the wild set bounds. However even though C is lower than 1.7, the actual runtime is worse, because of the additional restarts, and cycle detection logic. That means it requires more computing power and additional registers, at the machine level. If well optimized, it can beat 4-Kangaroo on a CPU + RAM, but on a GPU the added requirements (cycle detection, more registers) do not reach the efficiency (throughput) of Kangaroo method, which is simple and direct: jump forward, check if DP, repeat. I don't intend on ruining someone's tenure, but I also highly suspect that the paper about 5 and 7 kangaroos was not reviewed by supervisors or even by the peer community.. I also discovered that C can go as low as 1.05 if we also allow 1.05 sqrt(N) stored items, with 3 kangaroos alone, and only addition group operations. And still people believe BSGS would work faster. for whatever reason, with TB of RAM.. I let them continue believing that. Oh and BTW 3 & 4-kang methods are already "mirrored", this is basically the reason why C was brought down from 2.0 to 1.72.
Thanks, we all already know about that paper, and I discussed it a few times in some of my posts. I also implemented it (even in my public Python kangaroo script) and it indeed brings C to ~ 1.25 - 1.45 but at the expense of much more complex logic, since we can have cycles in the side by side walks, which need to be handled. In addition, that method completely ignores the cost of having to create new starting points, which is not something to be ignored (in fact, is very very costly). This problem is also mentioned in the 3 & 4 kangaroo methods paper (which was actually a follow up to the paper you cited). There is also a more recent "fix" to the Gaudry-Schost method (but it is written in Chinese) which brings down C a little bit more, by refining the wild set bounds. However even though C is lower than 1.7, the actual runtime is worse, because of the additional restarts, and cycle detection logic. That means it requires more computing power and additional registers, at the machine level. If well optimized, it can beat 4-Kangaroo on a CPU + RAM, but on a GPU the added requirements (cycle detection, more registers) do not reach the efficiency (throughput) of Kangaroo method, which is simple and direct: jump forward, check if DP, repeat. I don't intend on ruining someone's tenure, but I also highly suspect that the paper about 5 and 7 kangaroos was not reviewed by supervisors or even by the peer community.. I also discovered that C can go as low as 1.05 if we also allow 1.05 sqrt(N) stored items, with 3 kangaroos alone, and only addition group operations. And still people believe BSGS would work faster. for whatever reason, with TB of RAM.. I let them continue believing that. Oh and BTW 3 & 4-kang methods are already "mirrored", this is basically the reason why C was brought down from 2.0 to 1.72.

Thanks, we all already know about that paper, and I discussed it a few times in some of my posts. I also implemented it (even in my public Python kangaroo script) and it indeed brings C to ~ 1.25 - 1.45 but at the expense of much more complex logic, since we can have cycles in the side by side walks, which need to be handled. In addition, that method completely ignores the cost of having to create new starting points, which is not something to be ignored (in fact, is very very costly). This problem is also mentioned in the 3 & 4 kangaroo methods paper (which was actually a follow up to the paper you cited). There is also a more recent "fix" to the Gaudry-Schost method (but it is written in Chinese) which brings down C a little bit more, by refining the wild set bounds. However even though C is lower than 1.7, the actual runtime is worse, because of the additional restarts, and cycle detection logic. That means it requires more computing power and additional registers, at the machine level. If well optimized, it can beat 4-Kangaroo on a CPU + RAM, but on a GPU the added requirements (cycle detection, more registers) do not reach the efficiency (throughput) of Kangaroo method, which is simple and direct: jump forward, check if DP, repeat. I don't intend on ruining someone's tenure, but I also highly suspect that the paper about 5 and 7 kangaroos was not reviewed by supervisors or even by the peer community.. I also discovered that C can go as low as 1.05 if we also allow 1.05 sqrt(N) stored items, with 3 kangaroos alone, and only addition group operations. And still people believe BSGS would work faster. for whatever reason, with TB of RAM.. I let them continue believing that. Oh and BTW 3 & 4-kang methods are already "mirrored", this is basically the reason why C was brought down from 2.0 to 1.72.

Thanks, we all already know about that paper, and I discussed it a few times in some of my posts. I also implemented it (even in my public Python kangaroo script) and it indeed brings C to ~ 1.25 - 1.45 but at the expense of much more complex logic, since we can have cycles in the side by side walks, which need to be handled. In addition, that method completely ignores the cost of having to create new starting points, which is not something to be ignored (in fact, is very very costly). This problem is also mentioned in the 3 & 4 kangaroo methods paper (which was actually a follow up to the paper you cited). There is also a more recent "fix" to the Gaudry-Schost method (but it is written in Chinese) which brings down C a little bit more, by refining the wild set bounds. However even though C is lower than 1.7, the actual runtime is worse, because of the additional restarts, and cycle detection logic. That means it requires more computing power and additional registers, at the machine level. If well optimized, it can beat 4-Kangaroo on a CPU + RAM, but on a GPU the added requirements (cycle detection, more registers) do not reach the efficiency (throughput) of Kangaroo method, which is simple and direct: jump forward, check if DP, repeat. I don't intend on ruining someone's tenure, but I also highly suspect that the paper about 5 and 7 kangaroos was not reviewed by supervisors or even by the peer community.. I also discovered that C can go as low as 1.05 if we also allow 1.05 sqrt(N) stored items, with 3 kangaroos alone, and only addition group operations. And still people believe BSGS would work faster. for whatever reason, with TB of RAM.. I let them continue believing that. Oh and BTW 3 & 4-kang methods are already "mirrored", this is basically the reason why C was brought down from 2.0 to 1.72.