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1. Multidimensional adaptive & progressive indexes

Author

Nerone, M. (Matheus), Holanda, P.T. (Pedro), Almeida, E.C. (Eduardo) de, Manegold, S. (Stefan), Nerone, M. (Matheus), Holanda, P.T. (Pedro), Almeida, E.C. (Eduardo) de, and Manegold, S. (Stefan)

Abstract

Exploratory data analysis is the primary technique used by data scientists to extract knowledge from new data sets. This type of workload is composed of trial-and-error hypothesis-driven queries with a human in the loop. To keep up with the data scientist's productivity, the system must be capable of answering queries in interactive times. Given that these queries are highly selective multidimensional queries, multidimensional indexes are necessary to ensure low latency. However, creating the appropriate indexes is not a given due to the highly exploratory and interactive nature of such human-in-the-loop scenarios.In this paper, we identify four main objectives that are desirable for exploratory data analysis workloads: (1) low overhead over the initial queries, (2) low query variance (i.e., high robustness), (3) predictable index convergence, and (4) low total workload time. Given that not all of them can be achieved at the same time, we present three novel incremental multidimensional indexing techniques that represent three sample points on a Pareto front for this multi-objective optimization problem. (a) The Adaptive KD-Tree is designed to achieve the lowest total workload time at the expense of a higher indexing penalty for the initial queries, lack of robustness, and unpredictable convergence. (b) The Progressive KD-Tree has predictable convergence and a user-defined indexing cost for the initial queries. However, total workload time can be higher than with Adaptive KD-Trees, and per-query time still varies. (c) The Greedy Progressive KD-Tree aims at full robustness at the expense of only improving the per-query cost after full index convergence.Our extensive experimental evaluation using both synthetic and real-life data sets and workloads shows that (a) the Adaptive KD-Tree reduce

Published
2021
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2. Multidimensional adaptive & progressive indexes

Author

Nerone, M. (Matheus), Timbó Holanda, P.T. (Pedro), Almeida, E.C. (Eduardo) de, Manegold, S. (Stefan), Nerone, M. (Matheus), Timbó Holanda, P.T. (Pedro), Almeida, E.C. (Eduardo) de, and Manegold, S. (Stefan)

Abstract

Exploratory data analysis is the primary technique used by data scientists to extract knowledge from new data sets. This type of workload is composed of trial-and-error hypothesis-driven queries with a human in the loop. To keep up with the data scientist's productivity, the system must be capable of answering queries in interactive times. Given that these queries are highly selective multidimensional queries, multidimensional indexes are necessary to ensure low latency. However, creating the appropriate indexes is not a given due to the highly exploratory and interactive nature of such human-in-the-loop scenarios.In this paper, we identify four main objectives that are desirable for exploratory data analysis workloads: (1) low overhead over the initial queries, (2) low query variance (i.e., high robustness), (3) predictable index convergence, and (4) low total workload time. Given that not all of them can be achieved at the same time, we present three novel incremental multidimensional indexing techniques that represent three sample points on a Pareto front for this multi-objective optimization problem. (a) The Adaptive KD-Tree is designed to achieve the lowest total workload time at the expense of a higher indexing penalty for the initial queries, lack of robustness, and unpredictable convergence. (b) The Progressive KD-Tree has predictable convergence and a user-defined indexing cost for the initial queries. However, total workload time can be higher than with Adaptive KD-Trees, and per-query time still varies. (c) The Greedy Progressive KD-Tree aims at full robustness at the expense of only improving the per-query cost after full index convergence.Our extensive experimental evaluation using both synthetic and real-life data sets and workloads shows that (a) the Adaptive KD-Tree reduces total workload time by up to a factor 2 compared to the state-of-the-art, (b) the Progressive KD-Tree achieves predictable convergence with up to one order of magnitude lower init

Published
2021
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3. Long-term thermal sensitivity of Earth's tropical forests

Author

Sullivan, M.J.P., Lewis, S.L., Affum-Baffoe, K., Castilho, C., Costa, F., Sanchez, A.C., Ewango, C.E.N., Hubau, W., Marimon, B., Monteagudo-Mendoza, A., Qie, L., Sonké, B., Martinez, R.V., Baker, T.R., Brienen, R.J.W., Feldpausch, T.R., Galbraith, D., Gloor, M., Malhi, Y., Aiba, S.-I., Alexiades, M.N., Almeida, E.C., de Oliveira, E.A., Dávila, E.Á., Loayza, P.A., Andrade, A., Vieira, S.A., Aragão, L.E.O.C., Araujo-Murakami, A., Arets, E.J.M.M., Arroyo, L., Ashton, P., Aymard C, G., Baccaro, F.B., Banin, L.F., Baraloto, C., Camargo, P.B., Barlow, J., Barroso, J., Bastin, J.-F., Batterman, S.A., Beeckman, H., Begne, S.K., Bennett, A.C., Berenguer, E., Berry, N., Blanc, L., Boeckx, P., Bogaert, J., Bonal, D., Bongers, F., Bradford, M., Brearley, F.Q., Brncic, T., Brown, F., Burban, B., Camargo, J.L., Castro, W., Céron, C., Ribeiro, S.C., Moscoso, V.C., Chave, J., Chezeaux, E., Clark, C.J., de Souza, F.C., Collins, M., Comiskey, J.A., Valverde, F.C., Medina, M.C., da Costa, L., Dančák, M., Dargie, G.C., Davies, S., Cardozo, N.D., de Haulleville, T., de Medeiros, M.B., Del Aguila Pasquel, J., Derroire, G., Di Fiore, A., Doucet, J.-L., Dourdain, A., Droissant, V., Duque, L.F., Ekoungoulou, R., Elias, F., Erwin, T., Esquivel-Muelbert, A., Fauset, S., Ferreira, J., Llampazo, G.F., Foli, E., Ford, A., Gilpin, M., Hall, J.S., Hamer, K.C., Hamilton, A.C., Harris, D.J., Hart, T.B., Hédl, R., Herault, B., Herrera, R., Higuchi, N., Hladik, A., Coronado, E.H., Huamantupa-Chuquimaco, I., Huasco, W.H., Jeffery, K.J., Jimenez-Rojas, E., Kalamandeen, M., Djuikouo, M.N.K., Kearsley, E., Umetsu, R.K., Kho, L.K., Killeen, T., Kitayama, K., Klitgaard, B., Koch, A., Labrière, N., Laurance, W., Laurance, S., Leal, M.E., Levesley, A., Lima, A.J.N., Lisingo, J., Lopes, A.P., Lopez-Gonzalez, G., Lovejoy, T., Lovett, J.C., Lowe, R., Magnusson, W.E., Malumbres-Olarte, J., Manzatto, ÂG., Marimon B.H., Jr, Marshall, A.R., Marthews, T., de Almeida Reis, S.M., Maycock, C., Melgaço, K., Mendoza, C., Metali, F., Mihindou, V., Milliken, W., Mitchard, E.T.A., Morandi, P.S., Mossman, H.L., Nagy, L., Nascimento, H., Neill, D., Nilus, R., Vargas, P.N., Palacios, W., Camacho, N.P., Peacock, J., Pendry, C., Peñuela Mora, M.C., Pickavance, G.C., Pipoly, J., Pitman, N., Playfair, M., Poorter, L., Poulsen, J.R., Poulsen, A.D., Preziosi, R., Prieto, A., Primack, R.B., Ramírez-Angulo, H., Reitsma, J., Réjou-Méchain, M., Correa, Z.R., de Sousa, T.R., Bayona, L.R., Roopsind, A., Rudas, A., Rutishauser, E., Abu Salim, K., Salomão, R.P., Schietti, J., Sheil, D., Silva, R.C., Espejo, J.S., Valeria, C.S., Silveira, M., Simo-Droissart, M., Simon, M.F., Singh, J., Soto Shareva, Y.C., Stahl, C., Stropp, J., Sukri, R., Sunderland, T., Svátek, M., Swaine, M.D., Swamy, V., Taedoumg, H., Talbot, J., Taplin, J., Taylor, D., Ter Steege, H., Terborgh, J., Thomas, R., Thomas, S.C., Torres-Lezama, A., Umunay, P., Gamarra, L.V., van der Heijden, G., van der Hout, P., van der Meer, P., van Nieuwstadt, M., Verbeeck, H., Vernimmen, R., Vicentini, A., Vieira, I.C.G., Torre, E.V., Vleminckx, J., Vos, V., Wang, O., White, L.J.T., Willcock, S., Woods, J.T., Wortel, V., Young, K., Zagt, R., Zemagho, L., Zuidema, P.A., Zwerts, J.A., Phillips, O.L., Sullivan, M.J.P., Lewis, S.L., Affum-Baffoe, K., Castilho, C., Costa, F., Sanchez, A.C., Ewango, C.E.N., Hubau, W., Marimon, B., Monteagudo-Mendoza, A., Qie, L., Sonké, B., Martinez, R.V., Baker, T.R., Brienen, R.J.W., Feldpausch, T.R., Galbraith, D., Gloor, M., Malhi, Y., Aiba, S.-I., Alexiades, M.N., Almeida, E.C., de Oliveira, E.A., Dávila, E.Á., Loayza, P.A., Andrade, A., Vieira, S.A., Aragão, L.E.O.C., Araujo-Murakami, A., Arets, E.J.M.M., Arroyo, L., Ashton, P., Aymard C, G., Baccaro, F.B., Banin, L.F., Baraloto, C., Camargo, P.B., Barlow, J., Barroso, J., Bastin, J.-F., Batterman, S.A., Beeckman, H., Begne, S.K., Bennett, A.C., Berenguer, E., Berry, N., Blanc, L., Boeckx, P., Bogaert, J., Bonal, D., Bongers, F., Bradford, M., Brearley, F.Q., Brncic, T., Brown, F., Burban, B., Camargo, J.L., Castro, W., Céron, C., Ribeiro, S.C., Moscoso, V.C., Chave, J., Chezeaux, E., Clark, C.J., de Souza, F.C., Collins, M., Comiskey, J.A., Valverde, F.C., Medina, M.C., da Costa, L., Dančák, M., Dargie, G.C., Davies, S., Cardozo, N.D., de Haulleville, T., de Medeiros, M.B., Del Aguila Pasquel, J., Derroire, G., Di Fiore, A., Doucet, J.-L., Dourdain, A., Droissant, V., Duque, L.F., Ekoungoulou, R., Elias, F., Erwin, T., Esquivel-Muelbert, A., Fauset, S., Ferreira, J., Llampazo, G.F., Foli, E., Ford, A., Gilpin, M., Hall, J.S., Hamer, K.C., Hamilton, A.C., Harris, D.J., Hart, T.B., Hédl, R., Herault, B., Herrera, R., Higuchi, N., Hladik, A., Coronado, E.H., Huamantupa-Chuquimaco, I., Huasco, W.H., Jeffery, K.J., Jimenez-Rojas, E., Kalamandeen, M., Djuikouo, M.N.K., Kearsley, E., Umetsu, R.K., Kho, L.K., Killeen, T., Kitayama, K., Klitgaard, B., Koch, A., Labrière, N., Laurance, W., Laurance, S., Leal, M.E., Levesley, A., Lima, A.J.N., Lisingo, J., Lopes, A.P., Lopez-Gonzalez, G., Lovejoy, T., Lovett, J.C., Lowe, R., Magnusson, W.E., Malumbres-Olarte, J., Manzatto, ÂG., Marimon B.H., Jr, Marshall, A.R., Marthews, T., de Almeida Reis, S.M., Maycock, C., Melgaço, K., Mendoza, C., Metali, F., Mihindou, V., Milliken, W., Mitchard, E.T.A., Morandi, P.S., Mossman, H.L., Nagy, L., Nascimento, H., Neill, D., Nilus, R., Vargas, P.N., Palacios, W., Camacho, N.P., Peacock, J., Pendry, C., Peñuela Mora, M.C., Pickavance, G.C., Pipoly, J., Pitman, N., Playfair, M., Poorter, L., Poulsen, J.R., Poulsen, A.D., Preziosi, R., Prieto, A., Primack, R.B., Ramírez-Angulo, H., Reitsma, J., Réjou-Méchain, M., Correa, Z.R., de Sousa, T.R., Bayona, L.R., Roopsind, A., Rudas, A., Rutishauser, E., Abu Salim, K., Salomão, R.P., Schietti, J., Sheil, D., Silva, R.C., Espejo, J.S., Valeria, C.S., Silveira, M., Simo-Droissart, M., Simon, M.F., Singh, J., Soto Shareva, Y.C., Stahl, C., Stropp, J., Sukri, R., Sunderland, T., Svátek, M., Swaine, M.D., Swamy, V., Taedoumg, H., Talbot, J., Taplin, J., Taylor, D., Ter Steege, H., Terborgh, J., Thomas, R., Thomas, S.C., Torres-Lezama, A., Umunay, P., Gamarra, L.V., van der Heijden, G., van der Hout, P., van der Meer, P., van Nieuwstadt, M., Verbeeck, H., Vernimmen, R., Vicentini, A., Vieira, I.C.G., Torre, E.V., Vleminckx, J., Vos, V., Wang, O., White, L.J.T., Willcock, S., Woods, J.T., Wortel, V., Young, K., Zagt, R., Zemagho, L., Zuidema, P.A., Zwerts, J.A., and Phillips, O.L.

Abstract

The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (-9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth's climate.

Published
2020

4. The global abundance of tree palms

Author

Muscarella, R., Emilio, T., Phillips, O.L., Lewis, S.L., Slik, F., Baker, W.J., Couvreur, T.L.P., Eiserhardt, W.L., Svenning, J.-C., Affum-Baffoe, K., Aiba, S.-I., de Almeida, E.C., de Almeida, S.S., de Oliveira, E.A., Álvarez-Dávila, E., Alves, L.F., Alvez-Valles, C.M., Carvalho, F.A., Guarin, F.A., Andrade, A., Aragão, L.E.O.C., Murakami, A.A., Arroyo, L., Ashton, P.S., Corredor, G.A.A., Baker, T.R., de Camargo, P.B., Barlow, J., Bastin, J.-F., Bengone, N.N., Berenguer, E., Berry, N., Blanc, L., Böhning-Gaese, K., Bonal, D., Bongers, F., Bradford, M., Brambach, F., Brearley, F.Q., Brewer, S.W., Camargo, J.L.C., Campbell, D.G., Castilho, C.V., Castro, W., Catchpole, D., Cerón Martínez, C.E., Chen, S., Chhang, P., Cho, P., Chutipong, W., Clark, C., Collins, M., Comiskey, J.A., Medina, M.N.C., Costa, F.R.C., Culmsee, H., David-Higuita, H., Davidar, P., del Aguila-Pasquel, J., Derroire, G., Di Fiore, A., Van Do, T., Doucet, J.-L., Dourdain, A., Drake, D.R., Ensslin, A., Erwin, T., Ewango, C.E.N., Ewers, R.M., Fauset, S., Feldpausch, T.R., Ferreira, J., Ferreira, L.V., Fischer, M., Franklin, J., Fredriksson, G.M., Gillespie, T.W., Gilpin, M., Gonmadje, C., Gunatilleke, A.U.N., Hakeem, K.R., Hall, J.S., Hamer, K.C., Harris, D.J., Harrison, R.D., Hector, A., Hemp, A., Herault, B., Pizango, C.G.H., Coronado, E.N.H., Hubau, W., Hussain, M.S., Ibrahim, F.-H., Imai, N., Joly, C.A., Joseph, S., Anitha, K., Kartawinata, K., Kassi, J., Killeen, T.J., Kitayama, K., Klitgård, B.B., Kooyman, R., Labrière, N., Larney, E., Laumonier, Y., Laurance, S.G., Laurance, W.F., Lawes, M.J., Levesley, A., Lisingo, J., Lovejoy, T., Lovett, J.C., Lu, X., Lykke, A.M., Magnusson, W.E., Mahayani, N.P.D., Malhi, Y., Mansor, A., Peña, J.L.M., Marimon-Junior, B.H., Marshall, A.R., Melgaco, K., Bautista, C.M., Mihindou, V., Millet, J., Milliken, W., Mohandass, D., Mendoza, A.L.M., Mugerwa, B., Nagamasu, H., Nagy, L., Seuaturien, N., Nascimento, M.T., Neill, D.A., Neto, L.M., Nilus, R., Vargas, M.P.N., Nurtjahya, E., de Araújo, R.N.O., Onrizal, O., Palacios, W.A., Palacios-Ramos, S., Parren, M., Paudel, E., Morandi, P.S., Pennington, R.T., Pickavance, G., Pipoly J.J., III, Pitman, N.C.A., Poedjirahajoe, E., Poorter, L., Poulsen, J.R., Rama Chandra Prasad, P., Prieto, A., Puyravaud, J.-P., Qie, L., Quesada, C.A., Ramírez-Angulo, H., Razafimahaimodison, J.C., Reitsma, J.M., Requena-Rojas, E.J., Correa, Z.R., Rodriguez, C.R., Roopsind, A., Rovero, F., Rozak, A., Lleras, A.R., Rutishauser, E., Rutten, G., Punchi-Manage, R., Salomão, R.P., Van Sam, H., Sarker, S.K., Satdichanh, M., Schietti, J., Schmitt, C.B., Marimon, B.S., Senbeta, F., Nath Sharma, L., Sheil, D., Sierra, R., Silva-Espejo, J.E., Silveira, M., Sonké, B., Steininger, M.K., Steinmetz, R., Stévart, T., Sukumar, R., Sultana, A., Sunderland, T.C.H., Suresh, H.S., Tang, J., Tanner, E., ter Steege, H., Terborgh, J.W., Theilade, I., Timberlake, J., Torres-Lezama, A., Umunay, P., Uriarte, M., Gamarra, L.V., van de Bult, M., van der Hout, P., Martinez, R.V., Vieira, I.C.G., Vieira, S.A., Vilanova, E., Cayo, J.V., Wang, O., Webb, C.O., Webb, E.L., White, L., Whitfeld, T.J.S., Wich, S., Willcock, S., Wiser, S.K., Young, K.R., Zakaria, R., Zang, R., Zartman, C.E., Zo-Bi, I.C., Balslev, H., Muscarella, R., Emilio, T., Phillips, O.L., Lewis, S.L., Slik, F., Baker, W.J., Couvreur, T.L.P., Eiserhardt, W.L., Svenning, J.-C., Affum-Baffoe, K., Aiba, S.-I., de Almeida, E.C., de Almeida, S.S., de Oliveira, E.A., Álvarez-Dávila, E., Alves, L.F., Alvez-Valles, C.M., Carvalho, F.A., Guarin, F.A., Andrade, A., Aragão, L.E.O.C., Murakami, A.A., Arroyo, L., Ashton, P.S., Corredor, G.A.A., Baker, T.R., de Camargo, P.B., Barlow, J., Bastin, J.-F., Bengone, N.N., Berenguer, E., Berry, N., Blanc, L., Böhning-Gaese, K., Bonal, D., Bongers, F., Bradford, M., Brambach, F., Brearley, F.Q., Brewer, S.W., Camargo, J.L.C., Campbell, D.G., Castilho, C.V., Castro, W., Catchpole, D., Cerón Martínez, C.E., Chen, S., Chhang, P., Cho, P., Chutipong, W., Clark, C., Collins, M., Comiskey, J.A., Medina, M.N.C., Costa, F.R.C., Culmsee, H., David-Higuita, H., Davidar, P., del Aguila-Pasquel, J., Derroire, G., Di Fiore, A., Van Do, T., Doucet, J.-L., Dourdain, A., Drake, D.R., Ensslin, A., Erwin, T., Ewango, C.E.N., Ewers, R.M., Fauset, S., Feldpausch, T.R., Ferreira, J., Ferreira, L.V., Fischer, M., Franklin, J., Fredriksson, G.M., Gillespie, T.W., Gilpin, M., Gonmadje, C., Gunatilleke, A.U.N., Hakeem, K.R., Hall, J.S., Hamer, K.C., Harris, D.J., Harrison, R.D., Hector, A., Hemp, A., Herault, B., Pizango, C.G.H., Coronado, E.N.H., Hubau, W., Hussain, M.S., Ibrahim, F.-H., Imai, N., Joly, C.A., Joseph, S., Anitha, K., Kartawinata, K., Kassi, J., Killeen, T.J., Kitayama, K., Klitgård, B.B., Kooyman, R., Labrière, N., Larney, E., Laumonier, Y., Laurance, S.G., Laurance, W.F., Lawes, M.J., Levesley, A., Lisingo, J., Lovejoy, T., Lovett, J.C., Lu, X., Lykke, A.M., Magnusson, W.E., Mahayani, N.P.D., Malhi, Y., Mansor, A., Peña, J.L.M., Marimon-Junior, B.H., Marshall, A.R., Melgaco, K., Bautista, C.M., Mihindou, V., Millet, J., Milliken, W., Mohandass, D., Mendoza, A.L.M., Mugerwa, B., Nagamasu, H., Nagy, L., Seuaturien, N., Nascimento, M.T., Neill, D.A., Neto, L.M., Nilus, R., Vargas, M.P.N., Nurtjahya, E., de Araújo, R.N.O., Onrizal, O., Palacios, W.A., Palacios-Ramos, S., Parren, M., Paudel, E., Morandi, P.S., Pennington, R.T., Pickavance, G., Pipoly J.J., III, Pitman, N.C.A., Poedjirahajoe, E., Poorter, L., Poulsen, J.R., Rama Chandra Prasad, P., Prieto, A., Puyravaud, J.-P., Qie, L., Quesada, C.A., Ramírez-Angulo, H., Razafimahaimodison, J.C., Reitsma, J.M., Requena-Rojas, E.J., Correa, Z.R., Rodriguez, C.R., Roopsind, A., Rovero, F., Rozak, A., Lleras, A.R., Rutishauser, E., Rutten, G., Punchi-Manage, R., Salomão, R.P., Van Sam, H., Sarker, S.K., Satdichanh, M., Schietti, J., Schmitt, C.B., Marimon, B.S., Senbeta, F., Nath Sharma, L., Sheil, D., Sierra, R., Silva-Espejo, J.E., Silveira, M., Sonké, B., Steininger, M.K., Steinmetz, R., Stévart, T., Sukumar, R., Sultana, A., Sunderland, T.C.H., Suresh, H.S., Tang, J., Tanner, E., ter Steege, H., Terborgh, J.W., Theilade, I., Timberlake, J., Torres-Lezama, A., Umunay, P., Uriarte, M., Gamarra, L.V., van de Bult, M., van der Hout, P., Martinez, R.V., Vieira, I.C.G., Vieira, S.A., Vilanova, E., Cayo, J.V., Wang, O., Webb, C.O., Webb, E.L., White, L., Whitfeld, T.J.S., Wich, S., Willcock, S., Wiser, S.K., Young, K.R., Zakaria, R., Zang, R., Zartman, C.E., Zo-Bi, I.C., and Balslev, H.

Abstract

Aim: Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location: Tropical and subtropical moist forests. Time period: Current. Major taxa studied: Palms (Arecaceae). Methods: We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co-occurring non-palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results: On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long-term climate stability. Life-form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non-tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above-ground biomass, but the magnitude and direction of the effect require additional work. Conclusions: Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neot

Published
2020

5. Long-term thermal sensitivity of Earth's tropical forests

Author

Sullivan, M.J.P., Lewis, S.L., Affum-Baffoe, K., Castilho, C., Costa, F., Sanchez, A.C., Ewango, C.E.N., Hubau, W., Marimon, B., Monteagudo-Mendoza, A., Qie, L., Sonké, B., Martinez, R.V., Baker, T.R., Brienen, R.J.W., Feldpausch, T.R., Galbraith, D., Gloor, M., Malhi, Y., Aiba, S.-I., Alexiades, M.N., Almeida, E.C., de Oliveira, E.A., Dávila, E.Á., Loayza, P.A., Andrade, A., Vieira, S.A., Aragão, L.E.O.C., Araujo-Murakami, A., Arets, E.J.M.M., Arroyo, L., Ashton, P., Aymard C, G., Baccaro, F.B., Banin, L.F., Baraloto, C., Camargo, P.B., Barlow, J., Barroso, J., Bastin, J.-F., Batterman, S.A., Beeckman, H., Begne, S.K., Bennett, A.C., Berenguer, E., Berry, N., Blanc, L., Boeckx, P., Bogaert, J., Bonal, D., Bongers, F., Bradford, M., Brearley, F.Q., Brncic, T., Brown, F., Burban, B., Camargo, J.L., Castro, W., Céron, C., Ribeiro, S.C., Moscoso, V.C., Chave, J., Chezeaux, E., Clark, C.J., de Souza, F.C., Collins, M., Comiskey, J.A., Valverde, F.C., Medina, M.C., da Costa, L., Dančák, M., Dargie, G.C., Davies, S., Cardozo, N.D., de Haulleville, T., de Medeiros, M.B., Del Aguila Pasquel, J., Derroire, G., Di Fiore, A., Doucet, J.-L., Dourdain, A., Droissant, V., Duque, L.F., Ekoungoulou, R., Elias, F., Erwin, T., Esquivel-Muelbert, A., Fauset, S., Ferreira, J., Llampazo, G.F., Foli, E., Ford, A., Gilpin, M., Hall, J.S., Hamer, K.C., Hamilton, A.C., Harris, D.J., Hart, T.B., Hédl, R., Herault, B., Herrera, R., Higuchi, N., Hladik, A., Coronado, E.H., Huamantupa-Chuquimaco, I., Huasco, W.H., Jeffery, K.J., Jimenez-Rojas, E., Kalamandeen, M., Djuikouo, M.N.K., Kearsley, E., Umetsu, R.K., Kho, L.K., Killeen, T., Kitayama, K., Klitgaard, B., Koch, A., Labrière, N., Laurance, W., Laurance, S., Leal, M.E., Levesley, A., Lima, A.J.N., Lisingo, J., Lopes, A.P., Lopez-Gonzalez, G., Lovejoy, T., Lovett, J.C., Lowe, R., Magnusson, W.E., Malumbres-Olarte, J., Manzatto, ÂG., Marimon B.H., Jr, Marshall, A.R., Marthews, T., de Almeida Reis, S.M., Maycock, C., Melgaço, K., Mendoza, C., Metali, F., Mihindou, V., Milliken, W., Mitchard, E.T.A., Morandi, P.S., Mossman, H.L., Nagy, L., Nascimento, H., Neill, D., Nilus, R., Vargas, P.N., Palacios, W., Camacho, N.P., Peacock, J., Pendry, C., Peñuela Mora, M.C., Pickavance, G.C., Pipoly, J., Pitman, N., Playfair, M., Poorter, L., Poulsen, J.R., Poulsen, A.D., Preziosi, R., Prieto, A., Primack, R.B., Ramírez-Angulo, H., Reitsma, J., Réjou-Méchain, M., Correa, Z.R., de Sousa, T.R., Bayona, L.R., Roopsind, A., Rudas, A., Rutishauser, E., Abu Salim, K., Salomão, R.P., Schietti, J., Sheil, D., Silva, R.C., Espejo, J.S., Valeria, C.S., Silveira, M., Simo-Droissart, M., Simon, M.F., Singh, J., Soto Shareva, Y.C., Stahl, C., Stropp, J., Sukri, R., Sunderland, T., Svátek, M., Swaine, M.D., Swamy, V., Taedoumg, H., Talbot, J., Taplin, J., Taylor, D., Ter Steege, H., Terborgh, J., Thomas, R., Thomas, S.C., Torres-Lezama, A., Umunay, P., Gamarra, L.V., van der Heijden, G., van der Hout, P., van der Meer, P., van Nieuwstadt, M., Verbeeck, H., Vernimmen, R., Vicentini, A., Vieira, I.C.G., Torre, E.V., Vleminckx, J., Vos, V., Wang, O., White, L.J.T., Willcock, S., Woods, J.T., Wortel, V., Young, K., Zagt, R., Zemagho, L., Zuidema, P.A., Zwerts, J.A., Phillips, O.L., Sullivan, M.J.P., Lewis, S.L., Affum-Baffoe, K., Castilho, C., Costa, F., Sanchez, A.C., Ewango, C.E.N., Hubau, W., Marimon, B., Monteagudo-Mendoza, A., Qie, L., Sonké, B., Martinez, R.V., Baker, T.R., Brienen, R.J.W., Feldpausch, T.R., Galbraith, D., Gloor, M., Malhi, Y., Aiba, S.-I., Alexiades, M.N., Almeida, E.C., de Oliveira, E.A., Dávila, E.Á., Loayza, P.A., Andrade, A., Vieira, S.A., Aragão, L.E.O.C., Araujo-Murakami, A., Arets, E.J.M.M., Arroyo, L., Ashton, P., Aymard C, G., Baccaro, F.B., Banin, L.F., Baraloto, C., Camargo, P.B., Barlow, J., Barroso, J., Bastin, J.-F., Batterman, S.A., Beeckman, H., Begne, S.K., Bennett, A.C., Berenguer, E., Berry, N., Blanc, L., Boeckx, P., Bogaert, J., Bonal, D., Bongers, F., Bradford, M., Brearley, F.Q., Brncic, T., Brown, F., Burban, B., Camargo, J.L., Castro, W., Céron, C., Ribeiro, S.C., Moscoso, V.C., Chave, J., Chezeaux, E., Clark, C.J., de Souza, F.C., Collins, M., Comiskey, J.A., Valverde, F.C., Medina, M.C., da Costa, L., Dančák, M., Dargie, G.C., Davies, S., Cardozo, N.D., de Haulleville, T., de Medeiros, M.B., Del Aguila Pasquel, J., Derroire, G., Di Fiore, A., Doucet, J.-L., Dourdain, A., Droissant, V., Duque, L.F., Ekoungoulou, R., Elias, F., Erwin, T., Esquivel-Muelbert, A., Fauset, S., Ferreira, J., Llampazo, G.F., Foli, E., Ford, A., Gilpin, M., Hall, J.S., Hamer, K.C., Hamilton, A.C., Harris, D.J., Hart, T.B., Hédl, R., Herault, B., Herrera, R., Higuchi, N., Hladik, A., Coronado, E.H., Huamantupa-Chuquimaco, I., Huasco, W.H., Jeffery, K.J., Jimenez-Rojas, E., Kalamandeen, M., Djuikouo, M.N.K., Kearsley, E., Umetsu, R.K., Kho, L.K., Killeen, T., Kitayama, K., Klitgaard, B., Koch, A., Labrière, N., Laurance, W., Laurance, S., Leal, M.E., Levesley, A., Lima, A.J.N., Lisingo, J., Lopes, A.P., Lopez-Gonzalez, G., Lovejoy, T., Lovett, J.C., Lowe, R., Magnusson, W.E., Malumbres-Olarte, J., Manzatto, ÂG., Marimon B.H., Jr, Marshall, A.R., Marthews, T., de Almeida Reis, S.M., Maycock, C., Melgaço, K., Mendoza, C., Metali, F., Mihindou, V., Milliken, W., Mitchard, E.T.A., Morandi, P.S., Mossman, H.L., Nagy, L., Nascimento, H., Neill, D., Nilus, R., Vargas, P.N., Palacios, W., Camacho, N.P., Peacock, J., Pendry, C., Peñuela Mora, M.C., Pickavance, G.C., Pipoly, J., Pitman, N., Playfair, M., Poorter, L., Poulsen, J.R., Poulsen, A.D., Preziosi, R., Prieto, A., Primack, R.B., Ramírez-Angulo, H., Reitsma, J., Réjou-Méchain, M., Correa, Z.R., de Sousa, T.R., Bayona, L.R., Roopsind, A., Rudas, A., Rutishauser, E., Abu Salim, K., Salomão, R.P., Schietti, J., Sheil, D., Silva, R.C., Espejo, J.S., Valeria, C.S., Silveira, M., Simo-Droissart, M., Simon, M.F., Singh, J., Soto Shareva, Y.C., Stahl, C., Stropp, J., Sukri, R., Sunderland, T., Svátek, M., Swaine, M.D., Swamy, V., Taedoumg, H., Talbot, J., Taplin, J., Taylor, D., Ter Steege, H., Terborgh, J., Thomas, R., Thomas, S.C., Torres-Lezama, A., Umunay, P., Gamarra, L.V., van der Heijden, G., van der Hout, P., van der Meer, P., van Nieuwstadt, M., Verbeeck, H., Vernimmen, R., Vicentini, A., Vieira, I.C.G., Torre, E.V., Vleminckx, J., Vos, V., Wang, O., White, L.J.T., Willcock, S., Woods, J.T., Wortel, V., Young, K., Zagt, R., Zemagho, L., Zuidema, P.A., Zwerts, J.A., and Phillips, O.L.

Abstract

The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (-9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth's climate.

Published
2020

6. The global abundance of tree palms

Author

Muscarella, R., Emilio, T., Phillips, O.L., Lewis, S.L., Slik, F., Baker, W.J., Couvreur, T.L.P., Eiserhardt, W.L., Svenning, J.-C., Affum-Baffoe, K., Aiba, S.-I., de Almeida, E.C., de Almeida, S.S., de Oliveira, E.A., Álvarez-Dávila, E., Alves, L.F., Alvez-Valles, C.M., Carvalho, F.A., Guarin, F.A., Andrade, A., Aragão, L.E.O.C., Murakami, A.A., Arroyo, L., Ashton, P.S., Corredor, G.A.A., Baker, T.R., de Camargo, P.B., Barlow, J., Bastin, J.-F., Bengone, N.N., Berenguer, E., Berry, N., Blanc, L., Böhning-Gaese, K., Bonal, D., Bongers, F., Bradford, M., Brambach, F., Brearley, F.Q., Brewer, S.W., Camargo, J.L.C., Campbell, D.G., Castilho, C.V., Castro, W., Catchpole, D., Cerón Martínez, C.E., Chen, S., Chhang, P., Cho, P., Chutipong, W., Clark, C., Collins, M., Comiskey, J.A., Medina, M.N.C., Costa, F.R.C., Culmsee, H., David-Higuita, H., Davidar, P., del Aguila-Pasquel, J., Derroire, G., Di Fiore, A., Van Do, T., Doucet, J.-L., Dourdain, A., Drake, D.R., Ensslin, A., Erwin, T., Ewango, C.E.N., Ewers, R.M., Fauset, S., Feldpausch, T.R., Ferreira, J., Ferreira, L.V., Fischer, M., Franklin, J., Fredriksson, G.M., Gillespie, T.W., Gilpin, M., Gonmadje, C., Gunatilleke, A.U.N., Hakeem, K.R., Hall, J.S., Hamer, K.C., Harris, D.J., Harrison, R.D., Hector, A., Hemp, A., Herault, B., Pizango, C.G.H., Coronado, E.N.H., Hubau, W., Hussain, M.S., Ibrahim, F.-H., Imai, N., Joly, C.A., Joseph, S., Anitha, K., Kartawinata, K., Kassi, J., Killeen, T.J., Kitayama, K., Klitgård, B.B., Kooyman, R., Labrière, N., Larney, E., Laumonier, Y., Laurance, S.G., Laurance, W.F., Lawes, M.J., Levesley, A., Lisingo, J., Lovejoy, T., Lovett, J.C., Lu, X., Lykke, A.M., Magnusson, W.E., Mahayani, N.P.D., Malhi, Y., Mansor, A., Peña, J.L.M., Marimon-Junior, B.H., Marshall, A.R., Melgaco, K., Bautista, C.M., Mihindou, V., Millet, J., Milliken, W., Mohandass, D., Mendoza, A.L.M., Mugerwa, B., Nagamasu, H., Nagy, L., Seuaturien, N., Nascimento, M.T., Neill, D.A., Neto, L.M., Nilus, R., Vargas, M.P.N., Nurtjahya, E., de Araújo, R.N.O., Onrizal, O., Palacios, W.A., Palacios-Ramos, S., Parren, M., Paudel, E., Morandi, P.S., Pennington, R.T., Pickavance, G., Pipoly J.J., III, Pitman, N.C.A., Poedjirahajoe, E., Poorter, L., Poulsen, J.R., Rama Chandra Prasad, P., Prieto, A., Puyravaud, J.-P., Qie, L., Quesada, C.A., Ramírez-Angulo, H., Razafimahaimodison, J.C., Reitsma, J.M., Requena-Rojas, E.J., Correa, Z.R., Rodriguez, C.R., Roopsind, A., Rovero, F., Rozak, A., Lleras, A.R., Rutishauser, E., Rutten, G., Punchi-Manage, R., Salomão, R.P., Van Sam, H., Sarker, S.K., Satdichanh, M., Schietti, J., Schmitt, C.B., Marimon, B.S., Senbeta, F., Nath Sharma, L., Sheil, D., Sierra, R., Silva-Espejo, J.E., Silveira, M., Sonké, B., Steininger, M.K., Steinmetz, R., Stévart, T., Sukumar, R., Sultana, A., Sunderland, T.C.H., Suresh, H.S., Tang, J., Tanner, E., ter Steege, H., Terborgh, J.W., Theilade, I., Timberlake, J., Torres-Lezama, A., Umunay, P., Uriarte, M., Gamarra, L.V., van de Bult, M., van der Hout, P., Martinez, R.V., Vieira, I.C.G., Vieira, S.A., Vilanova, E., Cayo, J.V., Wang, O., Webb, C.O., Webb, E.L., White, L., Whitfeld, T.J.S., Wich, S., Willcock, S., Wiser, S.K., Young, K.R., Zakaria, R., Zang, R., Zartman, C.E., Zo-Bi, I.C., Balslev, H., Muscarella, R., Emilio, T., Phillips, O.L., Lewis, S.L., Slik, F., Baker, W.J., Couvreur, T.L.P., Eiserhardt, W.L., Svenning, J.-C., Affum-Baffoe, K., Aiba, S.-I., de Almeida, E.C., de Almeida, S.S., de Oliveira, E.A., Álvarez-Dávila, E., Alves, L.F., Alvez-Valles, C.M., Carvalho, F.A., Guarin, F.A., Andrade, A., Aragão, L.E.O.C., Murakami, A.A., Arroyo, L., Ashton, P.S., Corredor, G.A.A., Baker, T.R., de Camargo, P.B., Barlow, J., Bastin, J.-F., Bengone, N.N., Berenguer, E., Berry, N., Blanc, L., Böhning-Gaese, K., Bonal, D., Bongers, F., Bradford, M., Brambach, F., Brearley, F.Q., Brewer, S.W., Camargo, J.L.C., Campbell, D.G., Castilho, C.V., Castro, W., Catchpole, D., Cerón Martínez, C.E., Chen, S., Chhang, P., Cho, P., Chutipong, W., Clark, C., Collins, M., Comiskey, J.A., Medina, M.N.C., Costa, F.R.C., Culmsee, H., David-Higuita, H., Davidar, P., del Aguila-Pasquel, J., Derroire, G., Di Fiore, A., Van Do, T., Doucet, J.-L., Dourdain, A., Drake, D.R., Ensslin, A., Erwin, T., Ewango, C.E.N., Ewers, R.M., Fauset, S., Feldpausch, T.R., Ferreira, J., Ferreira, L.V., Fischer, M., Franklin, J., Fredriksson, G.M., Gillespie, T.W., Gilpin, M., Gonmadje, C., Gunatilleke, A.U.N., Hakeem, K.R., Hall, J.S., Hamer, K.C., Harris, D.J., Harrison, R.D., Hector, A., Hemp, A., Herault, B., Pizango, C.G.H., Coronado, E.N.H., Hubau, W., Hussain, M.S., Ibrahim, F.-H., Imai, N., Joly, C.A., Joseph, S., Anitha, K., Kartawinata, K., Kassi, J., Killeen, T.J., Kitayama, K., Klitgård, B.B., Kooyman, R., Labrière, N., Larney, E., Laumonier, Y., Laurance, S.G., Laurance, W.F., Lawes, M.J., Levesley, A., Lisingo, J., Lovejoy, T., Lovett, J.C., Lu, X., Lykke, A.M., Magnusson, W.E., Mahayani, N.P.D., Malhi, Y., Mansor, A., Peña, J.L.M., Marimon-Junior, B.H., Marshall, A.R., Melgaco, K., Bautista, C.M., Mihindou, V., Millet, J., Milliken, W., Mohandass, D., Mendoza, A.L.M., Mugerwa, B., Nagamasu, H., Nagy, L., Seuaturien, N., Nascimento, M.T., Neill, D.A., Neto, L.M., Nilus, R., Vargas, M.P.N., Nurtjahya, E., de Araújo, R.N.O., Onrizal, O., Palacios, W.A., Palacios-Ramos, S., Parren, M., Paudel, E., Morandi, P.S., Pennington, R.T., Pickavance, G., Pipoly J.J., III, Pitman, N.C.A., Poedjirahajoe, E., Poorter, L., Poulsen, J.R., Rama Chandra Prasad, P., Prieto, A., Puyravaud, J.-P., Qie, L., Quesada, C.A., Ramírez-Angulo, H., Razafimahaimodison, J.C., Reitsma, J.M., Requena-Rojas, E.J., Correa, Z.R., Rodriguez, C.R., Roopsind, A., Rovero, F., Rozak, A., Lleras, A.R., Rutishauser, E., Rutten, G., Punchi-Manage, R., Salomão, R.P., Van Sam, H., Sarker, S.K., Satdichanh, M., Schietti, J., Schmitt, C.B., Marimon, B.S., Senbeta, F., Nath Sharma, L., Sheil, D., Sierra, R., Silva-Espejo, J.E., Silveira, M., Sonké, B., Steininger, M.K., Steinmetz, R., Stévart, T., Sukumar, R., Sultana, A., Sunderland, T.C.H., Suresh, H.S., Tang, J., Tanner, E., ter Steege, H., Terborgh, J.W., Theilade, I., Timberlake, J., Torres-Lezama, A., Umunay, P., Uriarte, M., Gamarra, L.V., van de Bult, M., van der Hout, P., Martinez, R.V., Vieira, I.C.G., Vieira, S.A., Vilanova, E., Cayo, J.V., Wang, O., Webb, C.O., Webb, E.L., White, L., Whitfeld, T.J.S., Wich, S., Willcock, S., Wiser, S.K., Young, K.R., Zakaria, R., Zang, R., Zartman, C.E., Zo-Bi, I.C., and Balslev, H.

Abstract

Aim: Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location: Tropical and subtropical moist forests. Time period: Current. Major taxa studied: Palms (Arecaceae). Methods: We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co-occurring non-palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results: On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long-term climate stability. Life-form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non-tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above-ground biomass, but the magnitude and direction of the effect require additional work. Conclusions: Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neot

Published
2020

7. Cracking KD-Tree: The first multidimensional adaptive indexing

Author

Holanda, P.T. (Pedro), Nerone, M. (Matheus), Almeida, E.C. (Eduardo) de, Manegold, S. (Stefan), Holanda, P.T. (Pedro), Nerone, M. (Matheus), Almeida, E.C. (Eduardo) de, and Manegold, S. (Stefan)

Abstract

Workload-aware physical data access structures are crucial to achieve short response time with (exploratory) data analysis tasks as commonly required for Big Data and Data Science applications. Recently proposed techniques such as automatic index advisers (for a priori known static workloads) and query-driven adaptive incremental indexing (for a priori unknown dynamic workloads) form the state-of-the-art to build single-dimensional indexes for single-attribute query predicates. However, similar techniques for more demanding multi-attribute query predicates, which are vital for any data analysis task, have not been proposed, yet. In this paper, we present our on-going work on a new set of workload-adaptive indexing techniques that focus on creating multidimensional indexes. We present our proof-of-concept, the Cracking KD-Tree, an adaptive indexing approach that generates a KD-Tree based on multidimensional range query predicates. It works by incrementally creating partial multidimensional indexes as a by-product of query processing. The indexes are produced only on those parts of the data that are accessed, and their creation cost is effectively distributed across a stream of queries. Experimental results show that the Cracking KD-Tree is three times faster than creating a full KD-Tree, one order of magnitude faster than executing full scans and two orders of magnitude faster than using uni-dimensional full or adaptive indexes on multiple columns.

Published
2018
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8. Near-data filters: Taking another brick from the memory wall

Author

Gomes Tomé, D. (Diego), Kepe, T.R. (Tiago), Alves, M.A.Z. (Marco), Almeida, E.C. (Eduardo) de, Gomes Tomé, D. (Diego), Kepe, T.R. (Tiago), Alves, M.A.Z. (Marco), and Almeida, E.C. (Eduardo) de

Abstract

In this paper, we use the potential of the near-data parallel computing presented in the Hybrid Memory Cube (HMC) to process near-data query filters and mitigate the data movement through the memory hierarchy up to the x86 processor. In particular, we present a set of extensions to the HMC Instruction Set Architecture (ISA) to filter data in-memory. Our near-data filters support vector instructions and solve data and control dependencies internally in the memory: internal register bank and branch-less evaluation of data filters transform control-flow dependencies into data-flow dependencies (i.e., predicated execution). We implemented the near-data filters in the select scan operator and we discuss preliminary results for projection and join. Our experiments running the select scan achieve performance improvements of up to 5.64x with an average reduction of 80% in energy consumption when executing a micro-benchmark of the 1 GB TPC-H database.

Published
2018

9. HIPE: HMC Instruction Predication Extension Applied on Database Processing

Author

Gomes Tomé, D. (Diego), Santos, P.C. (Paulo), Carro, L. (Luigi), Almeida, E.C. (Eduardo) de, Alves, M.A.Z. (Marco), Gomes Tomé, D. (Diego), Santos, P.C. (Paulo), Carro, L. (Luigi), Almeida, E.C. (Eduardo) de, and Alves, M.A.Z. (Marco)

Abstract

The recent Hybrid Memory Cube (HMC) is a smart memory which includes functional units inside one logic layer of the 3D stacked memory design. In order to execute instructions inside the Hybrid Memory Cube (HMC), the processor needs to send instructions to be executed near data, keeping most of the pipeline complexity inside the processor. Thus, control-flow and data-flow dependencies are all managed inside the processor, in such way that only update instructions are supported by the HMC. In order to solve data-flow dependencies inside the memory, previous work proposed HMC Instruction Vector Extensions (HIVE), which embeds a high number of functional units with a interlock register bank. In this work we propose HMC Instruction Prediction Extensions (HIPE), that supports predicated execution inside the memory, in order to transform control-flow dependencies into data-flow dependencies. Our mechanism focus on removing the high latency iteration between the processor and the smart memory during the execution of branches that depends on data processed inside the memory. In this paper we evaluate a balanced design of HIVE comparing to x86 and HMC executions. After we show the HIPE mechanism results when executing a database workload, which is a strong candidate to use smart memories. We show interesting trade-offs of performance when comparing our mechanism to previous work.

Published
2018
Full Text
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10. Near-data filters: Taking another brick from the memory wall

Author

Gomes Tomé, D. (Diego), Kepe, T.R. (Tiago), Alves, M.A.Z. (Marco), Almeida, E.C. (Eduardo) de, Gomes Tomé, D. (Diego), Kepe, T.R. (Tiago), Alves, M.A.Z. (Marco), and Almeida, E.C. (Eduardo) de

Abstract

In this paper, we use the potential of the near-data parallel computing presented in the Hybrid Memory Cube (HMC) to process near-data query filters and mitigate the data movement through the memory hierarchy up to the x86 processor. In particular, we present a set of extensions to the HMC Instruction Set Architecture (ISA) to filter data in-memory. Our near-data filters support vector instructions and solve data and control dependencies internally in the memory: internal register bank and branch-less evaluation of data filters transform control-flow dependencies into data-flow dependencies (i.e., predicated execution). We implemented the near-data filters in the select scan operator and we discuss preliminary results for projection and join. Our experiments running the select scan achieve performance improvements of up to 5.64x with an average reduction of 80% in energy consumption when executing a micro-benchmark of the 1 GB TPC-H database.

Published
2018

11. HIPE: HMC Instruction Predication Extension Applied on Database Processing

Author

Gomes Tomé, D. (Diego), Santos, P.C. (Paulo), Carro, L. (Luigi), Almeida, E.C. (Eduardo) de, Alves, M.A.Z. (Marco), Gomes Tomé, D. (Diego), Santos, P.C. (Paulo), Carro, L. (Luigi), Almeida, E.C. (Eduardo) de, and Alves, M.A.Z. (Marco)

Abstract

The recent Hybrid Memory Cube (HMC) is a smart memory which includes functional units inside one logic layer of the 3D stacked memory design. In order to execute instructions inside the Hybrid Memory Cube (HMC), the processor needs to send instructions to be executed near data, keeping most of the pipeline complexity inside the processor. Thus, control-flow and data-flow dependencies are all managed inside the processor, in such way that only update instructions are supported by the HMC. In order to solve data-flow dependencies inside the memory, previous work proposed HMC Instruction Vector Extensions (HIVE), which embeds a high number of functional units with a interlock register bank. In this work we propose HMC Instruction Prediction Extensions (HIPE), that supports predicated execution inside the memory, in order to transform control-flow dependencies into data-flow dependencies. Our mechanism focus on removing the high latency iteration between the processor and the smart memory during the execution of branches that depends on data processed inside the memory. In this paper we evaluate a balanced design of HIVE comparing to x86 and HMC executions. After we show the HIPE mechanism results when executing a database workload, which is a strong candidate to use smart memories. We show interesting trade-offs of performance when comparing our mechanism to previous work.

Published
2018
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12. Cracking KD-Tree: The first multidimensional adaptive indexing

Author

Timbó Holanda, P.T. (Pedro), Nerone, M. (Matheus), Almeida, E.C. (Eduardo) de, Manegold, S. (Stefan), Timbó Holanda, P.T. (Pedro), Nerone, M. (Matheus), Almeida, E.C. (Eduardo) de, and Manegold, S. (Stefan)

Abstract

Workload-aware physical data access structures are crucial to achieve short response time with (exploratory) data analysis tasks as commonly required for Big Data and Data Science applications. Recently proposed techniques such as automatic index advisers (for a priori known static workloads) and query-driven adaptive incremental indexing (for a priori unknown dynamic workloads) form the state-of-the-art to build single-dimensional indexes for single-attribute query predicates. However, similar techniques for more demanding multi-attribute query predicates, which are vital for any data analysis task, have not been proposed, yet. In this paper, we present our on-going work on a new set of workload-adaptive indexing techniques that focus on creating multidimensional indexes. We present our proof-of-concept, the Cracking KD-Tree, an adaptive indexing approach that generates a KD-Tree based on multidimensional range query predicates. It works by incrementally creating partial multidimensional indexes as a by-product of query processing. The indexes are produced only on those parts of the data that are accessed, and their creation cost is effectively distributed across a stream of queries. Experimental results show that the Cracking KD-Tree is three times faster than creating a full KD-Tree, one order of magnitude faster than executing full scans and two orders of magnitude faster than using uni-dimensional full or adaptive indexes on multiple columns.

Published
2018
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13. Hyperdominance in Amazonian forest carbon cycling

Author

Fauset, Sophie, Johnson, Michelle O., Gloor, Emanuel, Baker, Timothy R., Monteagudo, Abel, Brienen, Roel Jacobus Wilhelmus, Feldpausch, Ted R., Lopez-Gonzalez, Gabriela, Malhi, Yadvinder, Ter Steege, Hans, Pitman, Nigel C. A., Baraloto, Christopher, Engel, Julien, Petronelli, Pascal, Andrade, Ana, Camargo, Jose Luis C., Laurance, Susan G.W., Laurance, William F., Chave, Jérôme, Allie, Elodie, Nuñez Vargas, Percy, Terborgh, John, Ruokolainen, Kalle, Silveira, Marcos, Aymard, Gerardo A., Arroyo, Luzmila, Bonal, Damien, Ramirez-Angulo, Hirma, Araujo-Murakami, Alejandro, Neill, David, Hérault, Bruno, Dourdain, Aurélie, Torres-Lezama, Armando, Marimon, Beatriz S., Salomão, Rafael P., Comiskey, James A., Rejou-Mechain, Maxime, Toledo, Marisol, Licona, Juan Carlos, Alarcón, Alfredo, Prieto, Adriana, Rudas, Agustin, van der Meer, Peter J., Killeen, Timothy J., Marimon, Ben Hur Junior, Poorter, Lourens, Boot, R.G.A., Stergios, Basil, Vilanova, Emilio, Costa, Flávia R.C, Levis, Caroline, Schietti, Juliana, Souza, Priscila, Groot, N., Arets, Eric J. M. M., Chama Moscoso, Victor, Castro, Wendeson, Honorio Coronado, Eurídice N., Peña-Claros, Marielos, Stahl, Clément, Barroso, Jorcely, Talbot, Joey, Guimarães Vieira, Ima Célia, Van Der Heijden, Geertje, Thomas, R., Vos, Vincent, Almeida, E.C., Alvarez Davila, Esteban, Aragao, Luiz E.O.C., Erwin, Terry, Morandi, Paulo, Almeida de Oliveira, Edmar, Valadaõ, Marco B.X., Zagt, Roderick, van der Hout, Peter, Alvarez Loayza, Patricia, Pipoly, John, Wang, Ophelia, Alexiades, Miguel N., Cerón, Carlos, Huamantupa-Chuquimaco, Isau, Di Fiore, Anthony, Peacock, Julie, Pallqui Camacho, Nadir C., Umetsu, Ricardo Keichi, Barbosa de Camargo, Plínio, Burnham, Robyn J., Herrera, Rafael, Quesada, Carlos Alberto, Stropp, Juliana, Vieira, Simone Aparecida, Steininger, Marc, Reynel Rodriguez, Carlos, Restrepo, Z., Esquivel Muelbert, Adriane, Lewis, Simon L., Pickavance, Georgia C., Phillips, Oliver L., Fauset, Sophie, Johnson, Michelle O., Gloor, Emanuel, Baker, Timothy R., Monteagudo, Abel, Brienen, Roel Jacobus Wilhelmus, Feldpausch, Ted R., Lopez-Gonzalez, Gabriela, Malhi, Yadvinder, Ter Steege, Hans, Pitman, Nigel C. A., Baraloto, Christopher, Engel, Julien, Petronelli, Pascal, Andrade, Ana, Camargo, Jose Luis C., Laurance, Susan G.W., Laurance, William F., Chave, Jérôme, Allie, Elodie, Nuñez Vargas, Percy, Terborgh, John, Ruokolainen, Kalle, Silveira, Marcos, Aymard, Gerardo A., Arroyo, Luzmila, Bonal, Damien, Ramirez-Angulo, Hirma, Araujo-Murakami, Alejandro, Neill, David, Hérault, Bruno, Dourdain, Aurélie, Torres-Lezama, Armando, Marimon, Beatriz S., Salomão, Rafael P., Comiskey, James A., Rejou-Mechain, Maxime, Toledo, Marisol, Licona, Juan Carlos, Alarcón, Alfredo, Prieto, Adriana, Rudas, Agustin, van der Meer, Peter J., Killeen, Timothy J., Marimon, Ben Hur Junior, Poorter, Lourens, Boot, R.G.A., Stergios, Basil, Vilanova, Emilio, Costa, Flávia R.C, Levis, Caroline, Schietti, Juliana, Souza, Priscila, Groot, N., Arets, Eric J. M. M., Chama Moscoso, Victor, Castro, Wendeson, Honorio Coronado, Eurídice N., Peña-Claros, Marielos, Stahl, Clément, Barroso, Jorcely, Talbot, Joey, Guimarães Vieira, Ima Célia, Van Der Heijden, Geertje, Thomas, R., Vos, Vincent, Almeida, E.C., Alvarez Davila, Esteban, Aragao, Luiz E.O.C., Erwin, Terry, Morandi, Paulo, Almeida de Oliveira, Edmar, Valadaõ, Marco B.X., Zagt, Roderick, van der Hout, Peter, Alvarez Loayza, Patricia, Pipoly, John, Wang, Ophelia, Alexiades, Miguel N., Cerón, Carlos, Huamantupa-Chuquimaco, Isau, Di Fiore, Anthony, Peacock, Julie, Pallqui Camacho, Nadir C., Umetsu, Ricardo Keichi, Barbosa de Camargo, Plínio, Burnham, Robyn J., Herrera, Rafael, Quesada, Carlos Alberto, Stropp, Juliana, Vieira, Simone Aparecida, Steininger, Marc, Reynel Rodriguez, Carlos, Restrepo, Z., Esquivel Muelbert, Adriane, Lewis, Simon L., Pickavance, Georgia C., and Phillips, Oliver L.

Abstract

While Amazonian forests are extraordinarily diverse, the abundance of trees is skewed strongly towards relatively few 'hyperdominant' species. In addition to their diversity, Amazonian trees are a key component of the global carbon cycle, assimilating and storing more carbon than any other ecosystem on Earth. Here we ask, using a unique data set of 530 forest plots, if the functions of storing and producing woody carbon are concentrated in a small number of tree species, whether the most abundant species also dominate carbon cycling, and whether dominant species are characterized by specific functional traits. We find that dominance of forest function is even more concentrated in a few species than is dominance of tree abundance, with only ≈1% of Amazon tree species responsible for 50% of carbon storage and productivity. Although those species that contribute most to biomass and productivity are often abundant, species maximum size is also influential, while the identity and ranking of dominant species varies by function and by region.

Published
2015

14. Hyperdominance in Amazonian forest carbon cycling

Author

Fauset, Sophie, Johnson, Michelle O., Gloor, Emanuel, Baker, Timothy R., Monteagudo, Abel, Brienen, Roel Jacobus Wilhelmus, Feldpausch, Ted R., Lopez-Gonzalez, Gabriela, Malhi, Yadvinder, Ter Steege, Hans, Pitman, Nigel C.A., Baraloto, Christopher, Engel, Julien, Petronelli, Pascal, Andrade, Ana, Camargo, Jose Luis C., Laurance, Susan G.W., Laurance, William F., Chave, Jérôme, Allie, Elodie, Nuñez Vargas, Percy, Terborgh, John, Ruokolainen, Kalle, Silveira, Marcos, Aymard, Gerardo A., Arroyo, Luzmila, Bonal, Damien, Ramirez-Angulo, Hirma, Araujo-Murakami, Alejandro, Neill, David, Hérault, Bruno, Dourdain, Aurélie, Torres-Lezama, Armando, Marimon, Beatriz Schwantes, Salomão, Rafael P., Comiskey, James A., Rejou-Mechain, Maxime, Toledo, Marisol, Licona, Juan Carlos, Alarcón, Alfredo, Prieto, Adriana, Rudas, Agustin, van der Meer, Peter J., Killeen, Timothy J., Marimon, Ben Hur Junior, Poorter, Lourens, Boot, R.G.A., Stergios, Basil, Vilanova, Emilio, Costa, Flávia R.C, Levis, Caroline, Schietti, Juliana, Souza, Priscila, Groot, N., Arets, Eric J. M. M., Chama Moscoso, Victor, Castro, Wendeson, Honorio Coronado, Eurídice N., Peña-Claros, Marielos, Stahl, Clément, Barroso, Jorcely, Talbot, Joey, Guimarães Vieira, Ima Célia, Van Der Heijden, Geertje, Thomas, R., Vos, Vincent, Almeida, E.C., Alvarez Davila, Esteban, Aragao, Luiz E.O.C., Erwin, Terry, Morandi, Paulo, Almeida de Oliveira, Edmar, Valadaõ, Marco B.X., Zagt, Roderick, van der Hout, Peter, Alvarez Loayza, Patricia, Pipoly, John, Wang, Ophelia, Alexiades, Miguel N., Cerón, Carlos, Huamantupa-Chuquimaco, Isau, Di Fiore, Anthony, Peacock, Julie, Pallqui Camacho, Nadir C., Umetsu, Ricardo Keichi, Barbosa de Camargo, Plínio, Burnham, Robyn J., Herrera, Rafael, Quesada, Carlos Alberto, Stropp, Juliana, Vieira, Simone Aparecida, Steininger, Marc, Reynel Rodriguez, Carlos, Restrepo, Z., Esquivel Muelbert, Adriane, Lewis, Simon L., Pickavance, Georgia C., Phillips, Oliver L., Fauset, Sophie, Johnson, Michelle O., Gloor, Emanuel, Baker, Timothy R., Monteagudo, Abel, Brienen, Roel Jacobus Wilhelmus, Feldpausch, Ted R., Lopez-Gonzalez, Gabriela, Malhi, Yadvinder, Ter Steege, Hans, Pitman, Nigel C.A., Baraloto, Christopher, Engel, Julien, Petronelli, Pascal, Andrade, Ana, Camargo, Jose Luis C., Laurance, Susan G.W., Laurance, William F., Chave, Jérôme, Allie, Elodie, Nuñez Vargas, Percy, Terborgh, John, Ruokolainen, Kalle, Silveira, Marcos, Aymard, Gerardo A., Arroyo, Luzmila, Bonal, Damien, Ramirez-Angulo, Hirma, Araujo-Murakami, Alejandro, Neill, David, Hérault, Bruno, Dourdain, Aurélie, Torres-Lezama, Armando, Marimon, Beatriz Schwantes, Salomão, Rafael P., Comiskey, James A., Rejou-Mechain, Maxime, Toledo, Marisol, Licona, Juan Carlos, Alarcón, Alfredo, Prieto, Adriana, Rudas, Agustin, van der Meer, Peter J., Killeen, Timothy J., Marimon, Ben Hur Junior, Poorter, Lourens, Boot, R.G.A., Stergios, Basil, Vilanova, Emilio, Costa, Flávia R.C, Levis, Caroline, Schietti, Juliana, Souza, Priscila, Groot, N., Arets, Eric J. M. M., Chama Moscoso, Victor, Castro, Wendeson, Honorio Coronado, Eurídice N., Peña-Claros, Marielos, Stahl, Clément, Barroso, Jorcely, Talbot, Joey, Guimarães Vieira, Ima Célia, Van Der Heijden, Geertje, Thomas, R., Vos, Vincent, Almeida, E.C., Alvarez Davila, Esteban, Aragao, Luiz E.O.C., Erwin, Terry, Morandi, Paulo, Almeida de Oliveira, Edmar, Valadaõ, Marco B.X., Zagt, Roderick, van der Hout, Peter, Alvarez Loayza, Patricia, Pipoly, John, Wang, Ophelia, Alexiades, Miguel N., Cerón, Carlos, Huamantupa-Chuquimaco, Isau, Di Fiore, Anthony, Peacock, Julie, Pallqui Camacho, Nadir C., Umetsu, Ricardo Keichi, Barbosa de Camargo, Plínio, Burnham, Robyn J., Herrera, Rafael, Quesada, Carlos Alberto, Stropp, Juliana, Vieira, Simone Aparecida, Steininger, Marc, Reynel Rodriguez, Carlos, Restrepo, Z., Esquivel Muelbert, Adriane, Lewis, Simon L., Pickavance, Georgia C., and Phillips, Oliver L.

Abstract

While Amazonian forests are extraordinarily diverse, the abundance of trees is skewed strongly towards relatively few 'hyperdominant' species. In addition to their diversity, Amazonian trees are a key component of the global carbon cycle, assimilating and storing more carbon than any other ecosystem on Earth. Here we ask, using a unique data set of 530 forest plots, if the functions of storing and producing woody carbon are concentrated in a small number of tree species, whether the most abundant species also dominate carbon cycling, and whether dominant species are characterized by specific functional traits. We find that dominance of forest function is even more concentrated in a few species than is dominance of tree abundance, with only ≈1% of Amazon tree species responsible for 50% of carbon storage and productivity. Although those species that contribute most to biomass and productivity are often abundant, species maximum size is also influential, while the identity and ranking of dominant species varies by function and by region.

Published
2015

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